lab_jupyter_content_course_similarity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML321ENSkillsNetwork32585014-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Content-based Course Recommender System using Course Similarities**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **45** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In one of the previous lab, you have learned and practiced how to calculate the similarity between two courses using Bag of Words (BoW) features. For example, the similarity between course1 `Machine Learning for Everyone` and course2 `Machine Learning for Beginners` are `75%` as shown below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML321EN-SkillsNetwork/labs/module\\_2/images/course_sim.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned before, the content-based recommender system is highly based on the similarity calculation among items. The similarity or closeness of items is measured based on the similarity in the content or features of those items. The course genres are important features, and in addition to that, the BoW value is another important type of feature to represent course textual content.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will apply the course similarities metric to recommend  new courses which are similar to a user's presently enrolled courses.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After completing this lab you will be able to:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   Obtain the similarity between courses from a course similarity matrix\n",
    "*   Use the course similarity matrix to find and recommend new courses which are similar to enrolled courses\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare and setup lab environment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first install and import the required libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn==0.11.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (0.11.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.1) (1.3.5)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.1) (3.5.3)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.1) (1.7.3)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.1) (1.21.6)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (21.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (3.0.9)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (8.1.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.1) (4.38.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.23->seaborn==0.11.1) (2022.6)\n",
      "Requirement already satisfied: typing-extensions in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn==0.11.1) (4.4.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn==0.11.1) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn==0.11.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# also set a random state\n",
    "rs = 123"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load a pre-made course similarity matrix. If you are interested, you could easily calculate such a similarity matrix by iterating through all possible course pairs and calculating their similarities.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML321EN-SkillsNetwork/labs/datasets/sim.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "0    0.143346  0.000000  0.024405  ...  0.012695  0.070225  0.058224   \n",
       "1    0.119251  0.044162  0.000000  ...  0.180593  0.124631  0.087187   \n",
       "2    0.154303  0.000000  0.000000  ...  0.040996  0.037796  0.013430   \n",
       "3    0.000000  0.000000  0.000000  ...  0.151882  0.420084  0.427908   \n",
       "4    0.085126  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "302  0.039467  0.036539  0.060474  ...  0.047186  0.116008  0.137406   \n",
       "303  0.119098  0.055132  0.045622  ...  0.177989  0.092381  0.145126   \n",
       "304  0.254274  0.094165  0.025974  ...  0.153128  0.105191  0.138692   \n",
       "305  0.082199  0.076102  0.055978  ...  0.149234  0.093962  0.162170   \n",
       "306  0.000000  0.106668  0.039231  ...  0.142850  0.250859  0.260731   \n",
       "\n",
       "          300       301       302       303       304       305       306  \n",
       "0    0.046610  0.025850  0.033944  0.076825  0.072898  0.039276  0.121113  \n",
       "1    0.093060  0.019354  0.028239  0.063911  0.138270  0.031367  0.076940  \n",
       "2    0.037630  0.000000  0.018270  0.082698  0.133400  0.012684  0.000000  \n",
       "3    0.055764  0.000000  0.094759  0.030638  0.017443  0.018796  0.158073  \n",
       "4    0.096877  0.000000  0.060474  0.030415  0.129871  0.000000  0.000000  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "302  0.211743  0.256220  1.000000  0.211520  0.168595  0.129766  0.263734  \n",
       "303  0.527636  0.249675  0.211520  1.000000  0.242269  0.416067  0.178384  \n",
       "304  0.281108  0.132977  0.168595  0.242269  1.000000  0.220159  0.128902  \n",
       "305  0.476644  0.222350  0.129766  0.416067  0.220159  1.000000  0.126274  \n",
       "306  0.099901  0.249325  0.263734  0.178384  0.128902  0.126274  1.000000  \n",
       "\n",
       "[307 rows x 307 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_df = pd.read_csv(sim_url)\n",
    "sim_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The similarity matrix is a real number, symmetric metric with each element representing the similarity value (ranged 0 to 1) between course index `i` and course index `j`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could use `seaborn` to visualize the similarity metric, and since it is symmetric, we can just show the triangular  matrix (lower left):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configure seaborn\n",
    "sns.set_theme(style=\"white\")\n",
    "mask = np.triu(np.ones_like(sim_df, dtype=bool))\n",
    "_, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "# Plot a similarity heat map\n",
    "sns.heatmap(sim_df, mask=mask, cmap=cmap, vmin=0.01, vmax=1, center=0,\n",
    "            square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the heatmap; there are many hot spots, which means many courses are similar to each other. Such patterns suggest that it is possible to build a recommender system based on course similarities.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at a quick example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's first load the course content and BoW dataset\n",
    "course_url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML321EN-SkillsNetwork/labs/datasets/course_processed.csv\"\n",
    "course_df = pd.read_csv(course_url)\n",
    "bow_url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML321EN-SkillsNetwork/labs/datasets/courses_bows.csv\"\n",
    "bow_df = pd.read_csv(bow_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doc_index</th>\n",
       "      <th>doc_id</th>\n",
       "      <th>token</th>\n",
       "      <th>bow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>ai</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>apps</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>build</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>cloud</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>coming</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   doc_index    doc_id   token  bow\n",
       "0          0  ML0201EN      ai    2\n",
       "1          0  ML0201EN    apps    2\n",
       "2          0  ML0201EN   build    2\n",
       "3          0  ML0201EN   cloud    1\n",
       "4          0  ML0201EN  coming    1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we want to mention that the matrix indices are course indices (such as `0, 1, 2, 3`). Very often we need to query the actual course ids (such as `ML0151EN` and `ML0101ENv3`) based on course indices and vice versa. We can save the course id's and indices into two dictionaries for late queries:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, based on the `doc_index` and `doc_id` columns, we create an index to id mapping and another id to index mapping in two Python dictionaries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create course id to index and index to id mappings\n",
    "def get_doc_dicts(bow_df):\n",
    "    grouped_df = bow_df.groupby(['doc_index', 'doc_id']).max().reset_index(drop=False)\n",
    "    idx_id_dict = grouped_df[['doc_id']].to_dict()['doc_id']\n",
    "    id_idx_dict = {v: k for k, v in idx_id_dict.items()}\n",
    "    del grouped_df\n",
    "    return idx_id_dict, id_idx_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now suppose we have two example courses:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COURSE_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>ML0151EN</td>\n",
       "      <td>machine learning with r</td>\n",
       "      <td>this machine learning with r course dives into...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    COURSE_ID                    TITLE  \\\n",
       "200  ML0151EN  machine learning with r   \n",
       "\n",
       "                                           DESCRIPTION  \n",
       "200  this machine learning with r course dives into...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "course1 = course_df[course_df['COURSE_ID'] == \"ML0151EN\"]\n",
    "course1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COURSE_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>ML0101ENv3</td>\n",
       "      <td>machine learning with python</td>\n",
       "      <td>machine learning can be an incredibly benefici...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      COURSE_ID                         TITLE  \\\n",
       "158  ML0101ENv3  machine learning with python   \n",
       "\n",
       "                                           DESCRIPTION  \n",
       "158  machine learning can be an incredibly benefici...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "course2 = course_df[course_df['COURSE_ID'] == \"ML0101ENv3\"]\n",
    "course2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From their titles we can see they are all about machine learning. As such, they should be very similar to each other. Let's try to find their similarity in the similarity matrix.\n",
    "\n",
    "With their course ids, we can use the `id_idx_dict` dictionary to query their row and column index on the similarity matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Course 1's index is 200 and Course 2's index is 158\n"
     ]
    }
   ],
   "source": [
    "idx_id_dict, id_idx_dict = get_doc_dicts(bow_df)\n",
    "idx1 = id_idx_dict[\"ML0151EN\"]\n",
    "idx2 = id_idx_dict[\"ML0101ENv3\"]\n",
    "print(f\"Course 1's index is {idx1} and Course 2's index is {idx2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can locate their similarity value in row 200 and col 158, `sim_matrix[200][158]`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_idx_dict['ML0201EN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ML0201EN'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_id_dict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_matrix = sim_df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6626221399549089"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim = sim_matrix[idx1][idx2]\n",
    "sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's about 66% meaning these two courses are quite similar to each other.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK: Find courses which are similar enough to your enrolled courses.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you know how to easily use the pre-computed similarity matrix to query the similarity between any two courses. Do you want to make some course recommendations for yourself?\n",
    "\n",
    "Let's assume you are an end-user of the online course platform and already audited or completed some courses previously. Next, you expect the system would recommend similar courses based on your enrollments history.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the full course list, choose any courses that may interest you, such as those machine learning related courses:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COURSE_ID</th>\n",
       "      <th>TITLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>robots are coming  build iot apps with watson  swift  and node red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ML0122EN</td>\n",
       "      <td>accelerating deep learning with gpu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GPXX0ZG0EN</td>\n",
       "      <td>consuming restful services using the reactive jax rs client</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RP0105EN</td>\n",
       "      <td>analyzing big data in r using apache spark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GPXX0Z2PEN</td>\n",
       "      <td>containerizing  packaging  and running a spring boot application</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CNSC02EN</td>\n",
       "      <td>cloud native security conference   data security</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DX0106EN</td>\n",
       "      <td>data science bootcamp with r for university proffesors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GPXX0FTCEN</td>\n",
       "      <td>learn how to use docker containers for iterative development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAVSCTEST1</td>\n",
       "      <td>scorm test 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GPXX06RFEN</td>\n",
       "      <td>create your first mongodb database</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GPXX0SDXEN</td>\n",
       "      <td>testing microservices with the arquillian managed container</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CC0271EN</td>\n",
       "      <td>cloud pak for integration essentials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WA0103EN</td>\n",
       "      <td>watson analytics for social media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DX0108EN</td>\n",
       "      <td>data science bootcamp with python for university professors  advance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GPXX0PICEN</td>\n",
       "      <td>create a cryptocurrency trading algorithm in python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DAI101EN</td>\n",
       "      <td>data   ai essentials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GPXX0W7KEN</td>\n",
       "      <td>securing java microservices with eclipse microprofile json web token  microprofile jwt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GPXX0QR3EN</td>\n",
       "      <td>enabling distributed tracing in microservices with zipkin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BD0145EN</td>\n",
       "      <td>sql access for hadoop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HCC105EN</td>\n",
       "      <td>hybrid cloud conference  ai pipelines lab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DE0205EN</td>\n",
       "      <td>dataops methodology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DS0132EN</td>\n",
       "      <td>data   ai  jumpstart your journey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OS0101EN</td>\n",
       "      <td>introduction to open source</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DS0201EN</td>\n",
       "      <td>end to end data science on cloudpak for data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BENTEST4</td>\n",
       "      <td>ai for everyone  master the basics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CC0210EN</td>\n",
       "      <td>serverless computing using cloud functions   developer i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PA0103EN</td>\n",
       "      <td>predicting customer satisfaction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>HCC104EN</td>\n",
       "      <td>hybrid cloud conference serverless lab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>GPXX0A1YEN</td>\n",
       "      <td>validating constraints for javabeans in java microservices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>TMP0105EN</td>\n",
       "      <td>getting started with the data  apache spark makers build</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PA0107EN</td>\n",
       "      <td>predicting financial performance of a company</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>DB0113EN</td>\n",
       "      <td>db2 fundamentals i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PA0109EN</td>\n",
       "      <td>using clustering methods for investment portfolio analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PHPM002EN</td>\n",
       "      <td>php web application on a lamp stack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GPXX03HFEN</td>\n",
       "      <td>fundamentals of javascript through rock paper scissors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RP0103</td>\n",
       "      <td>using r with databases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RP0103EN</td>\n",
       "      <td>using r with databases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BD0212EN</td>\n",
       "      <td>spark fundamentals ii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GPXX0IBEN</td>\n",
       "      <td>data science in insurance  basic statistical analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SECM03EN</td>\n",
       "      <td>apply end to end security to a cloud application</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SC0103EN</td>\n",
       "      <td>spark overview for scala analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>GPXX0YXHEN</td>\n",
       "      <td>testing a microprofile or jakarta ee application using microshed testing with an open liberty docker container</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>RP0151EN</td>\n",
       "      <td>r 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TA0105</td>\n",
       "      <td>text analytics 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>SW0201EN</td>\n",
       "      <td>how to build watson ai and swift apis and make money</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>TMP0106</td>\n",
       "      <td>data science bootcamp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>GPXX0BUBEN</td>\n",
       "      <td>insurance risk assessment with montecarlo method using apache spark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ST0201EN</td>\n",
       "      <td>statistics 201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ST0301EN</td>\n",
       "      <td>statistics 301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>SW0101EN</td>\n",
       "      <td>build swift mobile apps with watson ai services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>TMP0101EN</td>\n",
       "      <td>text analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>DW0101EN</td>\n",
       "      <td>introduction to machine learning with sound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>BD0143EN</td>\n",
       "      <td>using hbase for real time access to your big data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>WA0101EN</td>\n",
       "      <td>watson analytics 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>GPXX04HEEN</td>\n",
       "      <td>insurance business modelling and basic actuarial calculations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>BD0141EN</td>\n",
       "      <td>accessing hadoop data using hive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>CO0401EN</td>\n",
       "      <td>beyond the basics  istio and ibm cloud kubernetes service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ML0122ENv1</td>\n",
       "      <td>accelerating deep learning with gpu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>BD0151EN</td>\n",
       "      <td>text analytics 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>TA0106EN</td>\n",
       "      <td>text analytics at scale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>TMP107</td>\n",
       "      <td>data science bootcamp with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ML0111EN</td>\n",
       "      <td>machine learning with apache systemml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>GPXX048OEN</td>\n",
       "      <td>action classification task based on internet firewall logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CO0201EN</td>\n",
       "      <td>container   kubernetes essentials with ibm cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>GPXX01DCEN</td>\n",
       "      <td>data science in health care  advanced prognostication using neural networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>GPXX04XJEN</td>\n",
       "      <td>advanced machine   deep learning for spam classification task</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>GPXX0JZ4EN</td>\n",
       "      <td>visual data analysis in banking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>GPXX0ZYVEN</td>\n",
       "      <td>secure analysis of credit card dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>GPXX0ZMZEN</td>\n",
       "      <td>data science in health care  advanced machine learning classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>GPXX0742EN</td>\n",
       "      <td>network traffic anomaly detection  intrusion detection task</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>GPXX0KV4EN</td>\n",
       "      <td>getting started with node js</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>GPXX01RYEN</td>\n",
       "      <td>getting started with mysql command line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>CC0120EN</td>\n",
       "      <td>an introduction to ibm cloud satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>QC0101EN</td>\n",
       "      <td>introduction to quantum computing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>GPXX0YMEEN</td>\n",
       "      <td>launch an ai hotdog detector as a serverless python app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>GPXX0Q8AEN</td>\n",
       "      <td>exploratory data analysis  eda  with pandas in banking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>TA0105EN</td>\n",
       "      <td>text analytics 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>GPXX0XFQEN</td>\n",
       "      <td>create tables and load data in mysql using phpmyadmin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>GPXX07REN</td>\n",
       "      <td>relational model concepts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PA0101EN</td>\n",
       "      <td>predictive modeling fundamentals i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>BC0202EN</td>\n",
       "      <td>build an iot blockchain network for a supply chain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>BC0101EN</td>\n",
       "      <td>blockchain essentials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>GPXX0725EN</td>\n",
       "      <td>getting started with postgresql command line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>BC0201EN</td>\n",
       "      <td>ibm blockchain foundation developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>GPXX0MIIEN</td>\n",
       "      <td>keys and constraints in mysql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>BD0223EN</td>\n",
       "      <td>exploring spark s graphx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>GPXX0435EN</td>\n",
       "      <td>data science in health care  basic statistical analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>GPXX06ZLEN</td>\n",
       "      <td>create tables and load data in postgresql using pgadmin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>GPXX0QS6EN</td>\n",
       "      <td>monitoring the metrics of java microservices using eclipse microprofile metrics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>GPXX07YGEN</td>\n",
       "      <td>configuring microservices running in kubernetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>AI0111EN</td>\n",
       "      <td>game playing ai with swift for tensorflow  s4tf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>GPXX0QJFEN</td>\n",
       "      <td>enabling cross origin resource sharing  cors  in a restful java microservice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>GPXX0LLEEN</td>\n",
       "      <td>getting started with db2 on cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>BD0121EN</td>\n",
       "      <td>apache pig 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>BD0135EN</td>\n",
       "      <td>developing distributed applications using zookeeper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>BD0133EN</td>\n",
       "      <td>controlling hadoop jobs using oozie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>BD0131EN</td>\n",
       "      <td>moving data into hadoop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>BD0115EN</td>\n",
       "      <td>mapreduce and yarn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>GPXX04V3EN</td>\n",
       "      <td>deploy a web server using python and ibm cloud engine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>GPXX05P1EN</td>\n",
       "      <td>acknowledging messages using microprofile reactive messaging</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>GPXX0D14EN</td>\n",
       "      <td>build a personal movie recommender with django</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>GPXX0G3KEN</td>\n",
       "      <td>managing and injecting dependencies into java microservices using contexts and dependency injection  cdi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>BD0211EN</td>\n",
       "      <td>spark fundamentals i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>GPXX0HZ2EN</td>\n",
       "      <td>deploying microservices to kubernetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>GPXX04TNEN</td>\n",
       "      <td>getting started with open liberty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>GPXX0IHMEN</td>\n",
       "      <td>consuming restful java microservices asynchronously using eclipse microprofile rest client</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>ST0101EN</td>\n",
       "      <td>statistics 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>BD0153EN</td>\n",
       "      <td>analyzing big data with a spreadsheet ui</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>DP0101EN</td>\n",
       "      <td>openrefine 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>BD0137EN</td>\n",
       "      <td>solr 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>ML0120EN</td>\n",
       "      <td>deep learning with tensorflow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>GPXX0E3QEN</td>\n",
       "      <td>building fault tolerant microservices with the  fallback annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>GPXX0XV3EN</td>\n",
       "      <td>consuming restful java microservices with template interfaces using eclipse microprofile rest client</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>CB0201EN</td>\n",
       "      <td>build chatbots with watson assistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>CB0105ENv1</td>\n",
       "      <td>node red  basics to bots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>GPXX0NHZEN</td>\n",
       "      <td>normalization   keys   constraints in relational database</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>CL0101EN</td>\n",
       "      <td>ibm cloud essentials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>CO0301EN</td>\n",
       "      <td>getting started with microservices with istio and ibm cloud kubernetes service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>GPXX0XENEN</td>\n",
       "      <td>playing tictactoe with reinforcement learning and openai gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>GPXX0HAAEN</td>\n",
       "      <td>deploying a microservice to openshift by using a kubernetes operator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>DA0201EN</td>\n",
       "      <td>data analysis demos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>DJ0101EN</td>\n",
       "      <td>data journalism  first steps  skills and tools</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>CP0101EN</td>\n",
       "      <td>mathematical optimization for business problems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>DB0111EN</td>\n",
       "      <td>db2 academic training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>DB0115EN</td>\n",
       "      <td>db2 fundamentals ii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>COM001EN</td>\n",
       "      <td>scalable web applications on kubernetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>DA0151EN</td>\n",
       "      <td>data analysis using r 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>DB0151EN</td>\n",
       "      <td>nosql and dbaas 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>CO0302EN</td>\n",
       "      <td>kubernetes operators advanced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>GPXX0T0FEN</td>\n",
       "      <td>project  deploy a serverless app for image processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>DS0107</td>\n",
       "      <td>data science career talks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>DS0110EN</td>\n",
       "      <td>data science with open data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>DX0107EN</td>\n",
       "      <td>data science bootcamp with python for university professors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>DS0321EN</td>\n",
       "      <td>bitcoin 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>DS0105EN</td>\n",
       "      <td>data science hands on with open source tools</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>DS0103EN</td>\n",
       "      <td>data science methodology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>GPXX0I4FEN</td>\n",
       "      <td>creating asynchronous java microservices using microprofile reactive messaging</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>GPXX06KEEN</td>\n",
       "      <td>build a smart search form with algolia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>GPXX0YBFEN</td>\n",
       "      <td>documenting restful apis using microprofile openapi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>LB0109ENv1</td>\n",
       "      <td>reactive architecture  distributed messaging patterns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>GPXX0KHHEN</td>\n",
       "      <td>data science in agriculture  land use classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>LB0111EN</td>\n",
       "      <td>reactive architecture  cqrs   event sourcing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>ML0115EN</td>\n",
       "      <td>deep learning 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>LB0105ENv1</td>\n",
       "      <td>reactive architecture  reactive microservices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>LB0103ENv1</td>\n",
       "      <td>reactive architecture  domain driven design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>IT0101EN</td>\n",
       "      <td>building robots with tjbot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>CC0250EN</td>\n",
       "      <td>building cloud native and multicloud applications</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>BD0101EN</td>\n",
       "      <td>big data 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>EE0101EN</td>\n",
       "      <td>modernizing java ee applications</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>GPXX04P5EN</td>\n",
       "      <td>data science in agriculture  prognostication using by neural network</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>CO0101EN</td>\n",
       "      <td>docker essentials  a developer introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>ML0122ENv3</td>\n",
       "      <td>accelerating deep learning with gpus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>LB0107ENv1</td>\n",
       "      <td>reactive architecture  building scalable systems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>CB0101EN</td>\n",
       "      <td>build your own chatbots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>GPXX0PG8EN</td>\n",
       "      <td>data science in agriculture  basic statistical analysis and geo visualisation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>ML0101EN</td>\n",
       "      <td>machine learning with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>CB0103EN</td>\n",
       "      <td>build your own chatbot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>ML0109EN</td>\n",
       "      <td>machine learning   dimensionality reduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>ML0101ENv3</td>\n",
       "      <td>machine learning with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>GPXX0M7ZEN</td>\n",
       "      <td>consuming a restful java web service using json b and json p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>CC0121EN</td>\n",
       "      <td>an introduction to ibm cloud for financial services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>DA0101EN</td>\n",
       "      <td>data analysis with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>GPXX07UGEN</td>\n",
       "      <td>testing reactive java microservices using microshed testing framework</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>GPXX0QU9EN</td>\n",
       "      <td>checking the health of java microservices by using kubernetes readiness and liveness probes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>SC0101EN</td>\n",
       "      <td>scala 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>GPXX0QTEEN</td>\n",
       "      <td>checking the health of java microservices by using eclipse microprofile health check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>GPXX0WTIEN</td>\n",
       "      <td>train a hotdog image recognition model with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>GPXX08WYEN</td>\n",
       "      <td>externalizing configuration for java microservices using eclipse microprofile config</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>GPXX0UMSEN</td>\n",
       "      <td>integrating restful services with a reactive system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>GPXX0M6UEN</td>\n",
       "      <td>using the cql shell to execute keyspace operations in cassandra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>GPXX097UEN</td>\n",
       "      <td>performing table and crud operations with cassandra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>SN0111EN</td>\n",
       "      <td>how to create and publish guided projects and hands on labs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>GPXX0JGFEN</td>\n",
       "      <td>building a simple restful java microservice using jax rs and json b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>CO0193EN</td>\n",
       "      <td>hybrid cloud conference   backend services for containers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>GPXX0HC7EN</td>\n",
       "      <td>transform photos to sketches and paintings with opencv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>GPXX04MXEN</td>\n",
       "      <td>quick introduction to a b testing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>DS0101EN</td>\n",
       "      <td>introduction to data science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>LB0101ENv1</td>\n",
       "      <td>reactive architecture  introduction to reactive systems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>DS0301EN</td>\n",
       "      <td>data privacy fundamentals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>GPXX0RL8EN</td>\n",
       "      <td>consuming a restful java web service with angularjs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>GPXX05LMEN</td>\n",
       "      <td>implement consumer driven contract testing for java microservices using the pact framework</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>BD0111EN</td>\n",
       "      <td>hadoop 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>CC0101EN</td>\n",
       "      <td>introduction to cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>GPXX0UN5EN</td>\n",
       "      <td>classification of yelp reviews using sentiment analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>BD0221EN</td>\n",
       "      <td>spark mllib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>CC0103EN</td>\n",
       "      <td>ibm cloud essentials   v3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>GPXX0TY1EN</td>\n",
       "      <td>performing database operations in the cloudant dashboard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>GPXX0T3CEN</td>\n",
       "      <td>working with databases in ibm cloudant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>PY0101EN</td>\n",
       "      <td>python for data science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>CC0201EN</td>\n",
       "      <td>introduction to containers  kubernetes  and openshift v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>BD0123EN</td>\n",
       "      <td>simplifying data pipelines with apache kafka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>DB0101EN</td>\n",
       "      <td>sql and relational databases 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>GPXX05RDEN</td>\n",
       "      <td>medical appointment data analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>DV0151EN</td>\n",
       "      <td>data visualization with r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>GPXX0BSAEN</td>\n",
       "      <td>implementing a graphql microservice using microprofile api to query and update data from multiple services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>GPXX0G81EN</td>\n",
       "      <td>consuming a restful java web service with reactjs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>GPXX0FFCEN</td>\n",
       "      <td>building and testing a java web application with maven and open liberty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>GPXX0MP0EN</td>\n",
       "      <td>building a hypermedia driven restful java microservice using hypermedia as the engine of application state  hateoas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>DV0101EN</td>\n",
       "      <td>data visualization with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>ML0103EN</td>\n",
       "      <td>digital analytics   regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>ML0151EN</td>\n",
       "      <td>machine learning with r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>GPXX01AVEN</td>\n",
       "      <td>containerizing and running java microservices in docker containers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>ML0120ENv2</td>\n",
       "      <td>deep learning with tensorflow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>RP0101EN</td>\n",
       "      <td>r for data science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>GPXX0G31EN</td>\n",
       "      <td>accessing and persisting data in microservices using java persistence api  jpa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>GPXX0KY1EN</td>\n",
       "      <td>data science in health care  basic prognostication and geo visualization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>GPXX0JLHEN</td>\n",
       "      <td>enabling distributed tracing in java microservices using eclipse microprofile opentracing and the jaeger tracing system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>CC0150EN</td>\n",
       "      <td>building cloud native and multicloud applications</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>GPXX0QQ3EN</td>\n",
       "      <td>deploy an ai powered discord bot with a voice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>GPXX0RQLEN</td>\n",
       "      <td>views in postgresql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>GPXX0WRDEN</td>\n",
       "      <td>streaming updates from a microprofile reactive messaging microservice using server sent events  sse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>GPXX0ADEN</td>\n",
       "      <td>consuming a restful java web service with angular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>ML0120ENv3</td>\n",
       "      <td>deep learning with tensorflow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>SC0105EN</td>\n",
       "      <td>data science with scala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>excourse01</td>\n",
       "      <td>relational database systems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>excourse02</td>\n",
       "      <td>business intelligence and data warehousing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>excourse03</td>\n",
       "      <td>nosql systems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>excourse04</td>\n",
       "      <td>\\nsql for data science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>excourse05</td>\n",
       "      <td>\\ndistributed computing with spark sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>excourse06</td>\n",
       "      <td>\\nsql for data science capstone project</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>excourse07</td>\n",
       "      <td>database management essentials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>excourse08</td>\n",
       "      <td>using databases with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>excourse09</td>\n",
       "      <td>process data from dirty to clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>excourse10</td>\n",
       "      <td>database architecture  scale  and nosql with elasticsearch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>excourse11</td>\n",
       "      <td>the nature of data and relational database design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>excourse12</td>\n",
       "      <td>python scripting  files  inheritance  and databases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>excourse13</td>\n",
       "      <td>relational database support for data warehouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>excourse14</td>\n",
       "      <td>introduction to structured query language  sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>excourse15</td>\n",
       "      <td>crash course on python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>excourse16</td>\n",
       "      <td>programming for everybody  getting started with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>excourse17</td>\n",
       "      <td>python basics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>excourse18</td>\n",
       "      <td>python programming  a concise introduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>excourse19</td>\n",
       "      <td>introduction to python programming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>excourse20</td>\n",
       "      <td>python and statistics for financial analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>excourse21</td>\n",
       "      <td>applied machine learning in python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>excourse22</td>\n",
       "      <td>introduction to data science in python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>excourse23</td>\n",
       "      <td>data analysis using python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>excourse24</td>\n",
       "      <td>introduction to cloud computing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>excourse25</td>\n",
       "      <td>cloud computing basics  cloud 101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>excourse26</td>\n",
       "      <td>cloud computing foundations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>excourse27</td>\n",
       "      <td>cloud computing concepts  part 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>excourse28</td>\n",
       "      <td>fundamentals of cloud computing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>excourse29</td>\n",
       "      <td>cloud computing concepts  part 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>excourse30</td>\n",
       "      <td>cloud computing applications  part 1  cloud systems and infrastructure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>excourse31</td>\n",
       "      <td>cloud computing applications  part 2  big data and applications in the cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>excourse32</td>\n",
       "      <td>introduction to data analytics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>excourse33</td>\n",
       "      <td>excel basics for data analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>excourse34</td>\n",
       "      <td>introduction to data analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>excourse35</td>\n",
       "      <td>introduction to predictive modeling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>excourse36</td>\n",
       "      <td>data analysis using python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>excourse37</td>\n",
       "      <td>data analysis with r programming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>excourse38</td>\n",
       "      <td>data analysis with python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>excourse39</td>\n",
       "      <td>excel fundamentals for data analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>excourse40</td>\n",
       "      <td>exploratory data analysis for machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>excourse41</td>\n",
       "      <td>introduction to data analysis using excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>excourse42</td>\n",
       "      <td>big data analysis  hive  spark sql  dataframes and graphframes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>excourse43</td>\n",
       "      <td>cloud virtualization  containers and apis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>excourse44</td>\n",
       "      <td>alibaba cloud native solutions and container service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>excourse45</td>\n",
       "      <td>docker  basics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>excourse46</td>\n",
       "      <td>machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>excourse47</td>\n",
       "      <td>machine learning for all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>excourse48</td>\n",
       "      <td>introduction to machine learning  language processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>excourse49</td>\n",
       "      <td>applied machine learning in python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>excourse50</td>\n",
       "      <td>build  train  and deploy ml pipelines using bert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>excourse51</td>\n",
       "      <td>introduction to machine learning in production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>excourse52</td>\n",
       "      <td>machine learning data lifecycle in production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>excourse53</td>\n",
       "      <td>deploying machine learning models in production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>excourse54</td>\n",
       "      <td>exploratory data analysis for machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>excourse55</td>\n",
       "      <td>advanced computer vision with tensorflow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>excourse56</td>\n",
       "      <td>deep learning applications for computer vision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>excourse57</td>\n",
       "      <td>deep learning in computer vision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>excourse58</td>\n",
       "      <td>computer vision basics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>excourse59</td>\n",
       "      <td>fundamentals of digital image and video processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>excourse60</td>\n",
       "      <td>introduction to tensorflow for artificial intelligence  machine learning  and deep learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>excourse61</td>\n",
       "      <td>convolutional neural networks in tensorflow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>excourse62</td>\n",
       "      <td>introduction to data science in python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>excourse63</td>\n",
       "      <td>a crash course in data science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>excourse64</td>\n",
       "      <td>data science in real life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>excourse65</td>\n",
       "      <td>data science fundamentals for data analysts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>excourse66</td>\n",
       "      <td>executive data science capstone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>excourse67</td>\n",
       "      <td>introduction to big data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>excourse68</td>\n",
       "      <td>big data modeling and management systems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>excourse69</td>\n",
       "      <td>machine learning with big data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>excourse70</td>\n",
       "      <td>big data   capstone project</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>excourse71</td>\n",
       "      <td>big data essentials  hdfs  mapreduce and spark rdd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>excourse72</td>\n",
       "      <td>foundations for big data analysis with sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>excourse73</td>\n",
       "      <td>analyzing big data with sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>excourse74</td>\n",
       "      <td>fundamentals of big data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>excourse75</td>\n",
       "      <td>hadoop platform and application framework</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>excourse76</td>\n",
       "      <td>working with big data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>excourse77</td>\n",
       "      <td>natural language processing with attention models</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>excourse78</td>\n",
       "      <td>natural language processing with sequence models</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>excourse79</td>\n",
       "      <td>natural language processing with probabilistic models</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>excourse80</td>\n",
       "      <td>r programming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>excourse81</td>\n",
       "      <td>data analysis with r programming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>excourse82</td>\n",
       "      <td>getting started with data visualization in r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>excourse83</td>\n",
       "      <td>introduction to probability and data with r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>excourse84</td>\n",
       "      <td>using r for regression and machine learning in investment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>excourse85</td>\n",
       "      <td>data visualization in r with ggplot2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>excourse86</td>\n",
       "      <td>the r programming environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>excourse87</td>\n",
       "      <td>html  css  and javascript for web developers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>excourse88</td>\n",
       "      <td>javascript basics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>excourse89</td>\n",
       "      <td>javascript  jquery  and json</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>excourse90</td>\n",
       "      <td>programming foundations with javascript  html and css</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>excourse91</td>\n",
       "      <td>front end web development with react</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>excourse92</td>\n",
       "      <td>introduction to web development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>excourse93</td>\n",
       "      <td>interactivity with javascript and jquery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      COURSE_ID  \\\n",
       "0      ML0201EN   \n",
       "1      ML0122EN   \n",
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       "26     PA0103EN   \n",
       "27     HCC104EN   \n",
       "28   GPXX0A1YEN   \n",
       "29    TMP0105EN   \n",
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       "32     PA0109EN   \n",
       "33    PHPM002EN   \n",
       "34   GPXX03HFEN   \n",
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       "36     RP0103EN   \n",
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       "48     ST0301EN   \n",
       "49     SW0101EN   \n",
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       "52     BD0143EN   \n",
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       "54   GPXX04HEEN   \n",
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       "57   ML0122ENv1   \n",
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       "60       TMP107   \n",
       "61     ML0111EN   \n",
       "62   GPXX048OEN   \n",
       "63     CO0201EN   \n",
       "64   GPXX01DCEN   \n",
       "65   GPXX04XJEN   \n",
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       "69   GPXX0742EN   \n",
       "70   GPXX0KV4EN   \n",
       "71   GPXX01RYEN   \n",
       "72     CC0120EN   \n",
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       "74   GPXX0YMEEN   \n",
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       "89   GPXX07YGEN   \n",
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       "91   GPXX0QJFEN   \n",
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       "93     BD0121EN   \n",
       "94     BD0135EN   \n",
       "95     BD0133EN   \n",
       "96     BD0131EN   \n",
       "97     BD0115EN   \n",
       "98   GPXX04V3EN   \n",
       "99   GPXX05P1EN   \n",
       "100  GPXX0D14EN   \n",
       "101  GPXX0G3KEN   \n",
       "102    BD0211EN   \n",
       "103  GPXX0HZ2EN   \n",
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       "107    BD0153EN   \n",
       "108    DP0101EN   \n",
       "109    BD0137EN   \n",
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       "112  GPXX0XV3EN   \n",
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       "114  CB0105ENv1   \n",
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       "120    DA0201EN   \n",
       "121    DJ0101EN   \n",
       "122    CP0101EN   \n",
       "123    DB0111EN   \n",
       "124    DB0115EN   \n",
       "125    COM001EN   \n",
       "126    DA0151EN   \n",
       "127    DB0151EN   \n",
       "128    CO0302EN   \n",
       "129  GPXX0T0FEN   \n",
       "130      DS0107   \n",
       "131    DS0110EN   \n",
       "132    DX0107EN   \n",
       "133    DS0321EN   \n",
       "134    DS0105EN   \n",
       "135    DS0103EN   \n",
       "136  GPXX0I4FEN   \n",
       "137  GPXX06KEEN   \n",
       "138  GPXX0YBFEN   \n",
       "139  LB0109ENv1   \n",
       "140  GPXX0KHHEN   \n",
       "141    LB0111EN   \n",
       "142    ML0115EN   \n",
       "143  LB0105ENv1   \n",
       "144  LB0103ENv1   \n",
       "145    IT0101EN   \n",
       "146    CC0250EN   \n",
       "147    BD0101EN   \n",
       "148    EE0101EN   \n",
       "149  GPXX04P5EN   \n",
       "150    CO0101EN   \n",
       "151  ML0122ENv3   \n",
       "152  LB0107ENv1   \n",
       "153    CB0101EN   \n",
       "154  GPXX0PG8EN   \n",
       "155    ML0101EN   \n",
       "156    CB0103EN   \n",
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       "158  ML0101ENv3   \n",
       "159  GPXX0M7ZEN   \n",
       "160    CC0121EN   \n",
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       "171    SN0111EN   \n",
       "172  GPXX0JGFEN   \n",
       "173    CO0193EN   \n",
       "174  GPXX0HC7EN   \n",
       "175  GPXX04MXEN   \n",
       "176    DS0101EN   \n",
       "177  LB0101ENv1   \n",
       "178    DS0301EN   \n",
       "179  GPXX0RL8EN   \n",
       "180  GPXX05LMEN   \n",
       "181    BD0111EN   \n",
       "182    CC0101EN   \n",
       "183  GPXX0UN5EN   \n",
       "184    BD0221EN   \n",
       "185    CC0103EN   \n",
       "186  GPXX0TY1EN   \n",
       "187  GPXX0T3CEN   \n",
       "188    PY0101EN   \n",
       "189    CC0201EN   \n",
       "190    BD0123EN   \n",
       "191    DB0101EN   \n",
       "192  GPXX05RDEN   \n",
       "193    DV0151EN   \n",
       "194  GPXX0BSAEN   \n",
       "195  GPXX0G81EN   \n",
       "196  GPXX0FFCEN   \n",
       "197  GPXX0MP0EN   \n",
       "198    DV0101EN   \n",
       "199    ML0103EN   \n",
       "200    ML0151EN   \n",
       "201  GPXX01AVEN   \n",
       "202  ML0120ENv2   \n",
       "203    RP0101EN   \n",
       "204  GPXX0G31EN   \n",
       "205  GPXX0KY1EN   \n",
       "206  GPXX0JLHEN   \n",
       "207    CC0150EN   \n",
       "208  GPXX0QQ3EN   \n",
       "209  GPXX0RQLEN   \n",
       "210  GPXX0WRDEN   \n",
       "211   GPXX0ADEN   \n",
       "212  ML0120ENv3   \n",
       "213    SC0105EN   \n",
       "214  excourse01   \n",
       "215  excourse02   \n",
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       "217  excourse04   \n",
       "218  excourse05   \n",
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       "249  excourse36   \n",
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       "251  excourse38   \n",
       "252  excourse39   \n",
       "253  excourse40   \n",
       "254  excourse41   \n",
       "255  excourse42   \n",
       "256  excourse43   \n",
       "257  excourse44   \n",
       "258  excourse45   \n",
       "259  excourse46   \n",
       "260  excourse47   \n",
       "261  excourse48   \n",
       "262  excourse49   \n",
       "263  excourse50   \n",
       "264  excourse51   \n",
       "265  excourse52   \n",
       "266  excourse53   \n",
       "267  excourse54   \n",
       "268  excourse55   \n",
       "269  excourse56   \n",
       "270  excourse57   \n",
       "271  excourse58   \n",
       "272  excourse59   \n",
       "273  excourse60   \n",
       "274  excourse61   \n",
       "275  excourse62   \n",
       "276  excourse63   \n",
       "277  excourse64   \n",
       "278  excourse65   \n",
       "279  excourse66   \n",
       "280  excourse67   \n",
       "281  excourse68   \n",
       "282  excourse69   \n",
       "283  excourse70   \n",
       "284  excourse71   \n",
       "285  excourse72   \n",
       "286  excourse73   \n",
       "287  excourse74   \n",
       "288  excourse75   \n",
       "289  excourse76   \n",
       "290  excourse77   \n",
       "291  excourse78   \n",
       "292  excourse79   \n",
       "293  excourse80   \n",
       "294  excourse81   \n",
       "295  excourse82   \n",
       "296  excourse83   \n",
       "297  excourse84   \n",
       "298  excourse85   \n",
       "299  excourse86   \n",
       "300  excourse87   \n",
       "301  excourse88   \n",
       "302  excourse89   \n",
       "303  excourse90   \n",
       "304  excourse91   \n",
       "305  excourse92   \n",
       "306  excourse93   \n",
       "\n",
       "                                                                                                                        TITLE  \n",
       "0                                                          robots are coming  build iot apps with watson  swift  and node red  \n",
       "1                                                                                         accelerating deep learning with gpu  \n",
       "2                                                                 consuming restful services using the reactive jax rs client  \n",
       "3                                                                                  analyzing big data in r using apache spark  \n",
       "4                                                            containerizing  packaging  and running a spring boot application  \n",
       "5                                                                            cloud native security conference   data security  \n",
       "6                                                                      data science bootcamp with r for university proffesors  \n",
       "7                                                                learn how to use docker containers for iterative development  \n",
       "8                                                                                                                scorm test 1  \n",
       "9                                                                                          create your first mongodb database  \n",
       "10                                                                testing microservices with the arquillian managed container  \n",
       "11                                                                                       cloud pak for integration essentials  \n",
       "12                                                                                          watson analytics for social media  \n",
       "13                                                      data science bootcamp with python for university professors  advance   \n",
       "14                                                                        create a cryptocurrency trading algorithm in python  \n",
       "15                                                                                                       data   ai essentials  \n",
       "16                                    securing java microservices with eclipse microprofile json web token  microprofile jwt   \n",
       "17                                                                  enabling distributed tracing in microservices with zipkin  \n",
       "18                                                                                                      sql access for hadoop  \n",
       "19                                                                                  hybrid cloud conference  ai pipelines lab  \n",
       "20                                                                                                        dataops methodology  \n",
       "21                                                                                          data   ai  jumpstart your journey  \n",
       "22                                                                                                introduction to open source  \n",
       "23                                                                               end to end data science on cloudpak for data  \n",
       "24                                                                                         ai for everyone  master the basics  \n",
       "25                                                                   serverless computing using cloud functions   developer i  \n",
       "26                                                                                           predicting customer satisfaction  \n",
       "27                                                                                     hybrid cloud conference serverless lab  \n",
       "28                                                                 validating constraints for javabeans in java microservices  \n",
       "29                                                                   getting started with the data  apache spark makers build  \n",
       "30                                                                              predicting financial performance of a company  \n",
       "31                                                                                                         db2 fundamentals i  \n",
       "32                                                                 using clustering methods for investment portfolio analysis  \n",
       "33                                                                                        php web application on a lamp stack  \n",
       "34                                                                     fundamentals of javascript through rock paper scissors  \n",
       "35                                                                                                     using r with databases  \n",
       "36                                                                                                     using r with databases  \n",
       "37                                                                                                      spark fundamentals ii  \n",
       "38                                                                     data science in insurance  basic statistical analysis   \n",
       "39                                                                           apply end to end security to a cloud application  \n",
       "40                                                                                         spark overview for scala analytics  \n",
       "41             testing a microprofile or jakarta ee application using microshed testing with an open liberty docker container  \n",
       "42                                                                                                                      r 101  \n",
       "43                                                                                                         text analytics 101  \n",
       "44                                                                       how to build watson ai and swift apis and make money  \n",
       "45                                                                                                      data science bootcamp  \n",
       "46                                                       insurance risk assessment with montecarlo method using apache spark   \n",
       "47                                                                                                             statistics 201  \n",
       "48                                                                                                             statistics 301  \n",
       "49                                                                            build swift mobile apps with watson ai services  \n",
       "50                                                                                                              text analysis  \n",
       "51                                                                                introduction to machine learning with sound  \n",
       "52                                                                          using hbase for real time access to your big data  \n",
       "53                                                                                                       watson analytics 101  \n",
       "54                                                              insurance business modelling and basic actuarial calculations  \n",
       "55                                                                                           accessing hadoop data using hive  \n",
       "56                                                                  beyond the basics  istio and ibm cloud kubernetes service  \n",
       "57                                                                                        accelerating deep learning with gpu  \n",
       "58                                                                                                         text analytics 101  \n",
       "59                                                                                                    text analytics at scale  \n",
       "60                                                                                          data science bootcamp with python  \n",
       "61                                                                                      machine learning with apache systemml  \n",
       "62                                                                 action classification task based on internet firewall logs  \n",
       "63                                                                           container   kubernetes essentials with ibm cloud  \n",
       "64                                                data science in health care  advanced prognostication using neural networks  \n",
       "65                                                              advanced machine   deep learning for spam classification task  \n",
       "66                                                                                            visual data analysis in banking  \n",
       "67                                                                                     secure analysis of credit card dataset  \n",
       "68                                                      data science in health care  advanced machine learning classification  \n",
       "69                                                               network traffic anomaly detection  intrusion detection task   \n",
       "70                                                                                               getting started with node js  \n",
       "71                                                                                    getting started with mysql command line  \n",
       "72                                                                                     an introduction to ibm cloud satellite  \n",
       "73                                                                                          introduction to quantum computing  \n",
       "74                                                                    launch an ai hotdog detector as a serverless python app  \n",
       "75                                                                     exploratory data analysis  eda  with pandas in banking  \n",
       "76                                                                                                         text analytics 101  \n",
       "77                                                                      create tables and load data in mysql using phpmyadmin  \n",
       "78                                                                                                  relational model concepts  \n",
       "79                                                                                         predictive modeling fundamentals i  \n",
       "80                                                                         build an iot blockchain network for a supply chain  \n",
       "81                                                                                                      blockchain essentials  \n",
       "82                                                                               getting started with postgresql command line  \n",
       "83                                                                                        ibm blockchain foundation developer  \n",
       "84                                                                                              keys and constraints in mysql  \n",
       "85                                                                                                   exploring spark s graphx  \n",
       "86                                                                   data science in health care  basic statistical analysis   \n",
       "87                                                                    create tables and load data in postgresql using pgadmin  \n",
       "88                                            monitoring the metrics of java microservices using eclipse microprofile metrics  \n",
       "89                                                                            configuring microservices running in kubernetes  \n",
       "90                                                                           game playing ai with swift for tensorflow  s4tf   \n",
       "91                                               enabling cross origin resource sharing  cors  in a restful java microservice  \n",
       "92                                                                                          getting started with db2 on cloud  \n",
       "93                                                                                                             apache pig 101  \n",
       "94                                                                        developing distributed applications using zookeeper  \n",
       "95                                                                                        controlling hadoop jobs using oozie  \n",
       "96                                                                                                    moving data into hadoop  \n",
       "97                                                                                                         mapreduce and yarn  \n",
       "98                                                                      deploy a web server using python and ibm cloud engine  \n",
       "99                                                               acknowledging messages using microprofile reactive messaging  \n",
       "100                                                                            build a personal movie recommender with django  \n",
       "101                 managing and injecting dependencies into java microservices using contexts and dependency injection  cdi   \n",
       "102                                                                                                      spark fundamentals i  \n",
       "103                                                                                     deploying microservices to kubernetes  \n",
       "104                                                                                         getting started with open liberty  \n",
       "105                                consuming restful java microservices asynchronously using eclipse microprofile rest client  \n",
       "106                                                                                                            statistics 101  \n",
       "107                                                                                  analyzing big data with a spreadsheet ui  \n",
       "108                                                                                                            openrefine 101  \n",
       "109                                                                                                                  solr 101  \n",
       "110                                                                                             deep learning with tensorflow  \n",
       "111                                                       building fault tolerant microservices with the  fallback annotation  \n",
       "112                      consuming restful java microservices with template interfaces using eclipse microprofile rest client  \n",
       "113                                                                                      build chatbots with watson assistant  \n",
       "114                                                                                                  node red  basics to bots  \n",
       "115                                                                 normalization   keys   constraints in relational database  \n",
       "116                                                                                                      ibm cloud essentials  \n",
       "117                                            getting started with microservices with istio and ibm cloud kubernetes service  \n",
       "118                                                              playing tictactoe with reinforcement learning and openai gym  \n",
       "119                                                      deploying a microservice to openshift by using a kubernetes operator  \n",
       "120                                                                                                       data analysis demos  \n",
       "121                                                                            data journalism  first steps  skills and tools  \n",
       "122                                                                           mathematical optimization for business problems  \n",
       "123                                                                                                     db2 academic training  \n",
       "124                                                                                                       db2 fundamentals ii  \n",
       "125                                                                                   scalable web applications on kubernetes  \n",
       "126                                                                                                 data analysis using r 101  \n",
       "127                                                                                                       nosql and dbaas 101  \n",
       "128                                                                                             kubernetes operators advanced  \n",
       "129                                                                     project  deploy a serverless app for image processing  \n",
       "130                                                                                                 data science career talks  \n",
       "131                                                                                               data science with open data  \n",
       "132                                                               data science bootcamp with python for university professors  \n",
       "133                                                                                                               bitcoin 101  \n",
       "134                                                                              data science hands on with open source tools  \n",
       "135                                                                                                  data science methodology  \n",
       "136                                            creating asynchronous java microservices using microprofile reactive messaging  \n",
       "137                                                                                    build a smart search form with algolia  \n",
       "138                                                                       documenting restful apis using microprofile openapi  \n",
       "139                                                                     reactive architecture  distributed messaging patterns  \n",
       "140                                                                      data science in agriculture  land use classification  \n",
       "141                                                                              reactive architecture  cqrs   event sourcing  \n",
       "142                                                                                                         deep learning 101  \n",
       "143                                                                             reactive architecture  reactive microservices  \n",
       "144                                                                               reactive architecture  domain driven design  \n",
       "145                                                                                                building robots with tjbot  \n",
       "146                                                                         building cloud native and multicloud applications  \n",
       "147                                                                                                              big data 101  \n",
       "148                                                                                          modernizing java ee applications  \n",
       "149                                                      data science in agriculture  prognostication using by neural network  \n",
       "150                                                                               docker essentials  a developer introduction  \n",
       "151                                                                                      accelerating deep learning with gpus  \n",
       "152                                                                          reactive architecture  building scalable systems  \n",
       "153                                                                                                   build your own chatbots  \n",
       "154                                             data science in agriculture  basic statistical analysis and geo visualisation  \n",
       "155                                                                                              machine learning with python  \n",
       "156                                                                                                    build your own chatbot  \n",
       "157                                                                               machine learning   dimensionality reduction  \n",
       "158                                                                                              machine learning with python  \n",
       "159                                                              consuming a restful java web service using json b and json p  \n",
       "160                                                                       an introduction to ibm cloud for financial services  \n",
       "161                                                                                                 data analysis with python  \n",
       "162                                                     testing reactive java microservices using microshed testing framework  \n",
       "163                               checking the health of java microservices by using kubernetes readiness and liveness probes  \n",
       "164                                                                                                                 scala 101  \n",
       "165                                      checking the health of java microservices by using eclipse microprofile health check  \n",
       "166                                                                        train a hotdog image recognition model with python  \n",
       "167                                      externalizing configuration for java microservices using eclipse microprofile config  \n",
       "168                                                                       integrating restful services with a reactive system  \n",
       "169                                                           using the cql shell to execute keyspace operations in cassandra  \n",
       "170                                                                       performing table and crud operations with cassandra  \n",
       "171                                                               how to create and publish guided projects and hands on labs  \n",
       "172                                                       building a simple restful java microservice using jax rs and json b  \n",
       "173                                                                 hybrid cloud conference   backend services for containers  \n",
       "174                                                                    transform photos to sketches and paintings with opencv  \n",
       "175                                                                                         quick introduction to a b testing  \n",
       "176                                                                                              introduction to data science  \n",
       "177                                                                   reactive architecture  introduction to reactive systems  \n",
       "178                                                                                                 data privacy fundamentals  \n",
       "179                                                                       consuming a restful java web service with angularjs  \n",
       "180                                implement consumer driven contract testing for java microservices using the pact framework  \n",
       "181                                                                                                                hadoop 101  \n",
       "182                                                                                                     introduction to cloud  \n",
       "183                                                                   classification of yelp reviews using sentiment analysis  \n",
       "184                                                                                                               spark mllib  \n",
       "185                                                                                                 ibm cloud essentials   v3  \n",
       "186                                                                  performing database operations in the cloudant dashboard  \n",
       "187                                                                                    working with databases in ibm cloudant  \n",
       "188                                                                                                   python for data science  \n",
       "189                                                                  introduction to containers  kubernetes  and openshift v2  \n",
       "190                                                                              simplifying data pipelines with apache kafka  \n",
       "191                                                                                          sql and relational databases 101  \n",
       "192                                                                                         medical appointment data analysis  \n",
       "193                                                                                                 data visualization with r  \n",
       "194                implementing a graphql microservice using microprofile api to query and update data from multiple services  \n",
       "195                                                                         consuming a restful java web service with reactjs  \n",
       "196                                                   building and testing a java web application with maven and open liberty  \n",
       "197      building a hypermedia driven restful java microservice using hypermedia as the engine of application state  hateoas   \n",
       "198                                                                                            data visualization with python  \n",
       "199                                                                                            digital analytics   regression  \n",
       "200                                                                                                   machine learning with r  \n",
       "201                                                        containerizing and running java microservices in docker containers  \n",
       "202                                                                                             deep learning with tensorflow  \n",
       "203                                                                                                        r for data science  \n",
       "204                                           accessing and persisting data in microservices using java persistence api  jpa   \n",
       "205                                                  data science in health care  basic prognostication and geo visualization  \n",
       "206  enabling distributed tracing in java microservices using eclipse microprofile opentracing and the jaeger tracing system   \n",
       "207                                                                         building cloud native and multicloud applications  \n",
       "208                                                                             deploy an ai powered discord bot with a voice  \n",
       "209                                                                                                       views in postgresql  \n",
       "210                      streaming updates from a microprofile reactive messaging microservice using server sent events  sse   \n",
       "211                                                                         consuming a restful java web service with angular  \n",
       "212                                                                                             deep learning with tensorflow  \n",
       "213                                                                                                   data science with scala  \n",
       "214                                                                                               relational database systems  \n",
       "215                                                                                business intelligence and data warehousing  \n",
       "216                                                                                                             nosql systems  \n",
       "217                                                                                                    \\nsql for data science  \n",
       "218                                                                                    \\ndistributed computing with spark sql  \n",
       "219                                                                                   \\nsql for data science capstone project  \n",
       "220                                                                                            database management essentials  \n",
       "221                                                                                               using databases with python  \n",
       "222                                                                                          process data from dirty to clean  \n",
       "223                                                                database architecture  scale  and nosql with elasticsearch  \n",
       "224                                                                         the nature of data and relational database design  \n",
       "225                                                                       python scripting  files  inheritance  and databases  \n",
       "226                                                                           relational database support for data warehouses  \n",
       "227                                                                           introduction to structured query language  sql   \n",
       "228                                                                                                    crash course on python  \n",
       "229                                                                   programming for everybody  getting started with python   \n",
       "230                                                                                                             python basics  \n",
       "231                                                                                python programming  a concise introduction  \n",
       "232                                                                                        introduction to python programming  \n",
       "233                                                                              python and statistics for financial analysis  \n",
       "234                                                                                        applied machine learning in python  \n",
       "235                                                                                    introduction to data science in python  \n",
       "236                                                                                                data analysis using python  \n",
       "237                                                                                           introduction to cloud computing  \n",
       "238                                                                                        cloud computing basics  cloud 101   \n",
       "239                                                                                               cloud computing foundations  \n",
       "240                                                                                          cloud computing concepts  part 1  \n",
       "241                                                                                           fundamentals of cloud computing  \n",
       "242                                                                                          cloud computing concepts  part 2  \n",
       "243                                                    cloud computing applications  part 1  cloud systems and infrastructure  \n",
       "244                                              cloud computing applications  part 2  big data and applications in the cloud  \n",
       "245                                                                                            introduction to data analytics  \n",
       "246                                                                                            excel basics for data analysis  \n",
       "247                                                                                             introduction to data analysis  \n",
       "248                                                                                       introduction to predictive modeling  \n",
       "249                                                                                                data analysis using python  \n",
       "250                                                                                          data analysis with r programming  \n",
       "251                                                                                                 data analysis with python  \n",
       "252                                                                                      excel fundamentals for data analysis  \n",
       "253                                                                            exploratory data analysis for machine learning  \n",
       "254                                                                                 introduction to data analysis using excel  \n",
       "255                                                            big data analysis  hive  spark sql  dataframes and graphframes  \n",
       "256                                                                                 cloud virtualization  containers and apis  \n",
       "257                                                                      alibaba cloud native solutions and container service  \n",
       "258                                                                                                            docker  basics  \n",
       "259                                                                                                          machine learning  \n",
       "260                                                                                                  machine learning for all  \n",
       "261                                                                     introduction to machine learning  language processing  \n",
       "262                                                                                        applied machine learning in python  \n",
       "263                                                                          build  train  and deploy ml pipelines using bert  \n",
       "264                                                                            introduction to machine learning in production  \n",
       "265                                                                             machine learning data lifecycle in production  \n",
       "266                                                                           deploying machine learning models in production  \n",
       "267                                                                            exploratory data analysis for machine learning  \n",
       "268                                                                                  advanced computer vision with tensorflow  \n",
       "269                                                                            deep learning applications for computer vision  \n",
       "270                                                                                          deep learning in computer vision  \n",
       "271                                                                                                    computer vision basics  \n",
       "272                                                                        fundamentals of digital image and video processing  \n",
       "273                               introduction to tensorflow for artificial intelligence  machine learning  and deep learning  \n",
       "274                                                                               convolutional neural networks in tensorflow  \n",
       "275                                                                                    introduction to data science in python  \n",
       "276                                                                                            a crash course in data science  \n",
       "277                                                                                                 data science in real life  \n",
       "278                                                                               data science fundamentals for data analysts  \n",
       "279                                                                                           executive data science capstone  \n",
       "280                                                                                                  introduction to big data  \n",
       "281                                                                                  big data modeling and management systems  \n",
       "282                                                                                            machine learning with big data  \n",
       "283                                                                                               big data   capstone project  \n",
       "284                                                                        big data essentials  hdfs  mapreduce and spark rdd  \n",
       "285                                                                                foundations for big data analysis with sql  \n",
       "286                                                                                               analyzing big data with sql  \n",
       "287                                                                                                  fundamentals of big data  \n",
       "288                                                                                 hadoop platform and application framework  \n",
       "289                                                                                                     working with big data  \n",
       "290                                                                         natural language processing with attention models  \n",
       "291                                                                          natural language processing with sequence models  \n",
       "292                                                                     natural language processing with probabilistic models  \n",
       "293                                                                                                             r programming  \n",
       "294                                                                                          data analysis with r programming  \n",
       "295                                                                              getting started with data visualization in r  \n",
       "296                                                                               introduction to probability and data with r  \n",
       "297                                                                using r for regression and machine learning in investment   \n",
       "298                                                                                      data visualization in r with ggplot2  \n",
       "299                                                                                             the r programming environment  \n",
       "300                                                                              html  css  and javascript for web developers  \n",
       "301                                                                                                         javascript basics  \n",
       "302                                                                                              javascript  jquery  and json  \n",
       "303                                                                     programming foundations with javascript  html and css  \n",
       "304                                                                                      front end web development with react  \n",
       "305                                                                                           introduction to web development  \n",
       "306                                                                                  interactivity with javascript and jquery  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('max_colwidth', None)\n",
    "course_df[['COURSE_ID', 'TITLE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset pandas settings\n",
    "pd.reset_option('display.max_rows')\n",
    "pd.reset_option('max_colwidth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*TODO: Browse the course list and choose your interested courses*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "enrolled_course_ids = [\"excourse93\",\"GPXX05RDEN\",\"excourse92\",'excourse68','excourse69'] # add your interested coures id to the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COURSE_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>GPXX05RDEN</td>\n",
       "      <td>medical appointment data analysis</td>\n",
       "      <td>in this notebook we will try to analyze why wo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>excourse68</td>\n",
       "      <td>big data modeling and management systems</td>\n",
       "      <td>once you ve identified a big data issue to ana...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>excourse69</td>\n",
       "      <td>machine learning with big data</td>\n",
       "      <td>want to make sense of the volumes of data you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>excourse92</td>\n",
       "      <td>introduction to web development</td>\n",
       "      <td>this course is designed to start you on a path...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>excourse93</td>\n",
       "      <td>interactivity with javascript and jquery</td>\n",
       "      <td>this course is the third in our javascript for...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      COURSE_ID                                     TITLE  \\\n",
       "192  GPXX05RDEN         medical appointment data analysis   \n",
       "281  excourse68  big data modeling and management systems   \n",
       "282  excourse69            machine learning with big data   \n",
       "305  excourse92           introduction to web development   \n",
       "306  excourse93  interactivity with javascript and jquery   \n",
       "\n",
       "                                           DESCRIPTION  \n",
       "192  in this notebook we will try to analyze why wo...  \n",
       "281  once you ve identified a big data issue to ana...  \n",
       "282  want to make sense of the volumes of data you ...  \n",
       "305  this course is designed to start you on a path...  \n",
       "306  this course is the third in our javascript for...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enrolled_courses = course_df[course_df['COURSE_ID'].isin(enrolled_course_ids)]\n",
    "enrolled_courses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the full course list, we can find those unselected courses:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_courses = set(course_df['COURSE_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       " 'excourse55',\n",
       " 'excourse56',\n",
       " 'excourse57',\n",
       " 'excourse58',\n",
       " 'excourse59',\n",
       " 'excourse60',\n",
       " 'excourse61',\n",
       " 'excourse62',\n",
       " 'excourse63',\n",
       " 'excourse64',\n",
       " 'excourse65',\n",
       " 'excourse66',\n",
       " 'excourse67',\n",
       " 'excourse70',\n",
       " 'excourse71',\n",
       " 'excourse72',\n",
       " 'excourse73',\n",
       " 'excourse74',\n",
       " 'excourse75',\n",
       " 'excourse76',\n",
       " 'excourse77',\n",
       " 'excourse78',\n",
       " 'excourse79',\n",
       " 'excourse80',\n",
       " 'excourse81',\n",
       " 'excourse82',\n",
       " 'excourse83',\n",
       " 'excourse84',\n",
       " 'excourse85',\n",
       " 'excourse86',\n",
       " 'excourse87',\n",
       " 'excourse88',\n",
       " 'excourse89',\n",
       " 'excourse90',\n",
       " 'excourse91'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unselected_course_ids = all_courses.difference(enrolled_course_ids)\n",
    "unselected_course_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you can iterate each unselect course and check if it is similar enough to any of your selected courses. If the similarity is larger than a threshold such as 0.5 or 0.6, then add it to your course recommendation list:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*TODO: Complete the following method to recommend courses which are similar to your enrolled courses*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'excourse67': 0.8162431524411258,\n",
       " 'excourse32': 0.7463307735294326,\n",
       " 'excourse72': 0.7239396564289607,\n",
       " 'excourse74': 0.7156183028096771,\n",
       " 'DS0110EN': 0.6528639873857345,\n",
       " 'excourse23': 0.6470742121049355,\n",
       " 'excourse36': 0.6470742121049355,\n",
       " 'excourse04': 0.6450662226406081,\n",
       " 'excourse09': 0.6420363048520354,\n",
       " 'excourse33': 0.6278035386521185,\n",
       " 'excourse70': 0.6233858933462867,\n",
       " 'BD0101EN': 0.616759399492821,\n",
       " 'excourse02': 0.6122241715654336}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_recommendations_for_one_user(enrolled_course_ids, unselected_course_ids, id_idx_dict, sim_matrix):\n",
    "    # Create a dictionary to store your recommendation results\n",
    "    res = {}\n",
    "    threshold = 0.6\n",
    "    for enrolled_course in enrolled_course_ids:\n",
    "        for unselect_course in unselected_course_ids:\n",
    "            if enrolled_course in id_idx_dict and unselect_course in id_idx_dict:\n",
    "                \n",
    "                sim = 0\n",
    "                # Find the two indices for each enrolled_course and unselect_course, based on their two ids\n",
    "                # Calculate the similarity between an enrolled_course and an unselect_course\n",
    "                # e.g., Course ML0151EN's index is 200 and Course ML0101ENv3's index is 158\n",
    "                idx1 = id_idx_dict[enrolled_course]\n",
    "                idx2 = id_idx_dict[unselect_course]\n",
    "                # Find the similarity value from the sim_matrix\n",
    "                # sim = sim_matrix[200][158]\n",
    "                sim = sim_matrix[idx1][idx2]\n",
    "                if sim > threshold:\n",
    "                    if unselect_course not in res:\n",
    "                        res[unselect_course] = sim\n",
    "                    else:\n",
    "                        if sim >= res[unselect_course]:\n",
    "                            res[unselect_course] = sim\n",
    "                            \n",
    "    # Sort the results by similarity\n",
    "    res = {k: v for k, v in sorted(res.items(), key=lambda item: item[1], reverse=True)}\n",
    "    return res\n",
    "generate_recommendations_for_one_user(enrolled_course_ids,unselected_course_ids,id_idx_dict,sim_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The completed `generate_recommendations_user(...)` may ouput a dictionary like this:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'ML0151EN': 0.6626221399549089,\n",
    "'excourse47': 0.6347547807096177,\n",
    "'excourse46': 0.6120541193300345}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK: Generate course recommendations based on course similarities for all test uesrs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous task, you made some recommendations for yourself. Next, let's try to make recommendations for all the test users in the test dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_users_url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML321EN-SkillsNetwork/labs/datasets/rs_content_test.csv\"\n",
    "test_users_df = pd.read_csv(test_users_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at how many test users we have in the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total numbers of test users 1000\n"
     ]
    }
   ],
   "source": [
    "test_users = test_users_df.groupby(['user']).max().reset_index(drop=False)\n",
    "test_user_ids = test_users['user'].to_list()\n",
    "print(f\"Total numbers of test users {len(test_user_ids)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*TODO: Complete the `generate_recommendations_for_all()` method to generate recommendations for all users. You may implement the task with different solutions*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>item</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1502801</td>\n",
       "      <td>RP0105EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1609720</td>\n",
       "      <td>CNSC02EN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1347188</td>\n",
       "      <td>CO0301EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>755067</td>\n",
       "      <td>ML0103EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>538595</td>\n",
       "      <td>BD0115EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9397</th>\n",
       "      <td>1385217</td>\n",
       "      <td>EE0101EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9398</th>\n",
       "      <td>1864644</td>\n",
       "      <td>DA0101EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9399</th>\n",
       "      <td>435858</td>\n",
       "      <td>TMP0105EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9400</th>\n",
       "      <td>1888188</td>\n",
       "      <td>DB0101EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9401</th>\n",
       "      <td>708518</td>\n",
       "      <td>RP0101EN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9402 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user       item  rating\n",
       "0     1502801   RP0105EN     3.0\n",
       "1     1609720   CNSC02EN     2.0\n",
       "2     1347188   CO0301EN     3.0\n",
       "3      755067   ML0103EN     3.0\n",
       "4      538595   BD0115EN     3.0\n",
       "...       ...        ...     ...\n",
       "9397  1385217   EE0101EN     3.0\n",
       "9398  1864644   DA0101EN     3.0\n",
       "9399   435858  TMP0105EN     3.0\n",
       "9400  1888188   DB0101EN     3.0\n",
       "9401   708518   RP0101EN     3.0\n",
       "\n",
       "[9402 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_users_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COURSE_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ML0201EN</td>\n",
       "      <td>robots are coming  build iot apps with watson ...</td>\n",
       "      <td>have fun with iot and learn along the way  if ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ML0122EN</td>\n",
       "      <td>accelerating deep learning with gpu</td>\n",
       "      <td>training complex deep learning models with lar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GPXX0ZG0EN</td>\n",
       "      <td>consuming restful services using the reactive ...</td>\n",
       "      <td>learn how to use a reactive jax rs client to a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RP0105EN</td>\n",
       "      <td>analyzing big data in r using apache spark</td>\n",
       "      <td>apache spark is a popular cluster computing fr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GPXX0Z2PEN</td>\n",
       "      <td>containerizing  packaging  and running a sprin...</td>\n",
       "      <td>learn how to containerize  package  and run a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>excourse89</td>\n",
       "      <td>javascript  jquery  and json</td>\n",
       "      <td>in this course  we ll look at the javascript l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>excourse90</td>\n",
       "      <td>programming foundations with javascript  html ...</td>\n",
       "      <td>learn foundational programming concepts  e g  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>excourse91</td>\n",
       "      <td>front end web development with react</td>\n",
       "      <td>this course explores javascript based front en...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>excourse92</td>\n",
       "      <td>introduction to web development</td>\n",
       "      <td>this course is designed to start you on a path...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>excourse93</td>\n",
       "      <td>interactivity with javascript and jquery</td>\n",
       "      <td>this course is the third in our javascript for...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>307 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      COURSE_ID                                              TITLE  \\\n",
       "0      ML0201EN  robots are coming  build iot apps with watson ...   \n",
       "1      ML0122EN                accelerating deep learning with gpu   \n",
       "2    GPXX0ZG0EN  consuming restful services using the reactive ...   \n",
       "3      RP0105EN         analyzing big data in r using apache spark   \n",
       "4    GPXX0Z2PEN  containerizing  packaging  and running a sprin...   \n",
       "..          ...                                                ...   \n",
       "302  excourse89                       javascript  jquery  and json   \n",
       "303  excourse90  programming foundations with javascript  html ...   \n",
       "304  excourse91               front end web development with react   \n",
       "305  excourse92                    introduction to web development   \n",
       "306  excourse93           interactivity with javascript and jquery   \n",
       "\n",
       "                                           DESCRIPTION  \n",
       "0    have fun with iot and learn along the way  if ...  \n",
       "1    training complex deep learning models with lar...  \n",
       "2    learn how to use a reactive jax rs client to a...  \n",
       "3    apache spark is a popular cluster computing fr...  \n",
       "4    learn how to containerize  package  and run a ...  \n",
       "..                                                 ...  \n",
       "302  in this course  we ll look at the javascript l...  \n",
       "303  learn foundational programming concepts  e g  ...  \n",
       "304  this course explores javascript based front en...  \n",
       "305  this course is designed to start you on a path...  \n",
       "306  this course is the third in our javascript for...  \n",
       "\n",
       "[307 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "course_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WRITE YOUR CODE HERE\n",
    "def generate_recommendations_for_all():\n",
    "    users = []\n",
    "    courses = []\n",
    "    sim_scores = []\n",
    "    # Test user dataframe\n",
    "    test_users_df = pd.read_csv(test_users_url)\n",
    "    # Course similarity matrix\n",
    "    sim_df = pd.read_csv(sim_url)\n",
    "    sim_matrix = sim_df.to_numpy()\n",
    "    # Course content dataframe\n",
    "    course_df = pd.read_csv(course_url)\n",
    "    # Course BoW features\n",
    "    bow_df = pd.read_csv(bow_url)\n",
    "    test_users = test_users_df.groupby(['user']).max().reset_index(drop=False)\n",
    "    test_user_ids = test_users['user'].to_list() # list of users unique IDs\n",
    "    idx_id_dict, id_idx_dict = get_doc_dicts(bow_df)\n",
    "\n",
    "    # ...\n",
    "    all_courses = set(course_df['COURSE_ID'])\n",
    "    for user_id in test_user_ids:\n",
    "        \n",
    "        # For each user, call generate_recommendations_for_one_user() to generate the recommendation results\n",
    "        # Save the result to courses, sim_scores list\n",
    "        enrolled_course_ids = test_users_df[test_users_df.user==user_id].item.tolist() \n",
    "        unselected_course_ids = all_courses.difference(enrolled_course_ids)\n",
    "        outcome = generate_recommendations_for_one_user(enrolled_course_ids, unselected_course_ids, id_idx_dict, sim_matrix) \n",
    "        for key, value in outcome.items():\n",
    "            users.append(user_id)\n",
    "            courses.append(key) # courses names\n",
    "            sim_scores.append(value) # courses similarity index\n",
    "            pass\n",
    "            \n",
    "    \n",
    "    return users, courses, sim_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you completed the `generate_recommendations_for_all()` function, you can call it to save the results into a dataframe:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_dict = {}\n",
    "users, courses, sim_scores = generate_recommendations_for_all()\n",
    "res_dict['USER'] = users\n",
    "res_dict['COURSE_ID'] = courses\n",
    "res_dict['SCORE'] = sim_scores\n",
    "res_df = pd.DataFrame(res_dict, columns=['USER', 'COURSE_ID', 'SCORE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER</th>\n",
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       "      <td>37465</td>\n",
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       "      <td>0.650071</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11372</th>\n",
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       "      <td>excourse28</td>\n",
       "      <td>0.623754</td>\n",
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       "    <tr>\n",
       "      <th>11373</th>\n",
       "      <td>2087663</td>\n",
       "      <td>BD0145EN</td>\n",
       "      <td>0.623544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11374</th>\n",
       "      <td>2087663</td>\n",
       "      <td>excourse68</td>\n",
       "      <td>0.616759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11375</th>\n",
       "      <td>2087663</td>\n",
       "      <td>excourse60</td>\n",
       "      <td>0.615568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11376</th>\n",
       "      <td>2087663</td>\n",
       "      <td>excourse25</td>\n",
       "      <td>0.600535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11377 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          USER   COURSE_ID     SCORE\n",
       "0        37465    DS0110EN  0.732941\n",
       "1        37465  excourse67  0.708214\n",
       "2        37465  excourse63  0.694563\n",
       "3        37465  excourse72  0.652535\n",
       "4        37465  excourse74  0.650071\n",
       "...        ...         ...       ...\n",
       "11372  2087663  excourse28  0.623754\n",
       "11373  2087663    BD0145EN  0.623544\n",
       "11374  2087663  excourse68  0.616759\n",
       "11375  2087663  excourse60  0.615568\n",
       "11376  2087663  excourse25  0.600535\n",
       "\n",
       "[11377 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the previous user profile and course genre lab, with the recommendations generated for each user, you need to write some extra analytic code to answer the following questions:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   On average, how many new/unseen courses have been recommended to each user?\n",
    "*   What are the most frequently recommended courses? Return the top-10 commonly recommended courses across all users?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USER\n",
       "1314655    33\n",
       "1784245    32\n",
       "1953163    31\n",
       "700521     29\n",
       "1342117    29\n",
       "           ..\n",
       "565505      1\n",
       "1496193     1\n",
       "284008      1\n",
       "134697      1\n",
       "1781984     1\n",
       "Name: COURSE_ID, Length: 983, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = res_df.groupby('USER')['COURSE_ID'].size().sort_values(ascending=False) # number of courses recommended to each user\n",
    "user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.573753814852493"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user.mean() #average course recommended per user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COURSE_ID\n",
       "excourse62    579\n",
       "excourse22    579\n",
       "DS0110EN      562\n",
       "excourse65    555\n",
       "excourse63    555\n",
       "excourse72    551\n",
       "excourse68    550\n",
       "excourse74    539\n",
       "excourse67    539\n",
       "BD0145EN      506\n",
       "excourse47    352\n",
       "excourse46    352\n",
       "excourse32    329\n",
       "excourse09    329\n",
       "excourse23    329\n",
       "excourse36    329\n",
       "excourse33    329\n",
       "excourse38    329\n",
       "excourse24    302\n",
       "excourse60    268\n",
       "ML0122ENv3    262\n",
       "ML0151EN      251\n",
       "DV0151EN      225\n",
       "excourse28    198\n",
       "excourse25    198\n",
       "CB0101EN      181\n",
       "ML0120ENv3    156\n",
       "ML0120EN      110\n",
       "ML0122ENv1    105\n",
       "CL0101EN      104\n",
       "WA0103EN      101\n",
       "CC0103EN       93\n",
       "ML0120ENv2     60\n",
       "CO0401EN       54\n",
       "ML0101ENv3     53\n",
       "TA0105         48\n",
       "BD0151EN       45\n",
       "TMP0101EN      45\n",
       "TA0105EN       44\n",
       "DV0101EN       42\n",
       "LB0105ENv1     29\n",
       "DX0106EN       21\n",
       "excourse04     21\n",
       "TMP107         21\n",
       "TMP0106        21\n",
       "DS0107         21\n",
       "DS0201EN       19\n",
       "DS0132EN       16\n",
       "CO0301EN       12\n",
       "BD0101EN       12\n",
       "DS0105EN       12\n",
       "LB0101ENv1      8\n",
       "ML0122EN        8\n",
       "CB0103EN        7\n",
       "DS0101EN        4\n",
       "DAI101EN        3\n",
       "HCC104EN        2\n",
       "HCC105EN        1\n",
       "ML0115EN        1\n",
       "Name: USER, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cour = res_df.groupby('COURSE_ID')['USER'].size().sort_values(ascending=False) # number of users a course is recommended to\n",
    "cour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192.83050847457628"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cour.mean() #average number of users a course is recommended to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COURSE_ID\n",
       "excourse62    579\n",
       "excourse22    579\n",
       "DS0110EN      562\n",
       "excourse65    555\n",
       "excourse63    555\n",
       "excourse72    551\n",
       "excourse68    550\n",
       "excourse74    539\n",
       "excourse67    539\n",
       "BD0145EN      506\n",
       "Name: USER, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cour_top = cour.head(10) #Top ten courses\n",
    "cour_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, suppose we have only 3 test users, each user receives the following recommendations:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   User1: \\['course1', 'course2']\n",
    "*   User2: \\['course3', 'course4']\n",
    "*   User3: \\['course3', 'course4', 'course5']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the average recommended courses per user is $(2 + 2 + 3) / 3 = 2.33$. The top-2 recommended courses are: `course3`: 2 times, and `course4`: 2 times.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the answers may depend on your similarity threshold (default is 0.6). A lower similarity threshold yields more recommended courses but with smaller irrelevance.\n",
    "\n",
    "Ideally, we should limit the maximum course recommendations for each user to be less than 20 courses per user.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Yan Luo](https://www.linkedin.com/in/yan-luo-96288783/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML321ENSkillsNetwork32585014-2022-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description          |\n",
    "| ----------------- | ------- | ---------- | --------------------------- |\n",
    "| 2021-10-25        | 1.0     | Yan        | Created the initial version |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2021 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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