Machine Learning Zoomcamp (by DataTalks.Club - alexeygrigorev)

Syllabus

1. Introduction to Machine Learning

• 1.1 Introduction to Machine Learning ✔
• 1.2 ML vs Rule-Based Systems ✔
• 1.3 Supervised Machine Learning ✔
• 1.4 CRISP-DM ✔
• 1.5 Model Selection Process ✔
• 1.6 Setting up the Environment ✔
• 1.7 Introduction to NumPy ✔
• 1.8 Linear Algebra Refresher ✔
• 1.9 Introduction to Pandas ✔
• Summary ✔

2. Machine Learning for Regression

• 2.1 Car price prediction project ✔
• 2.2 Data preparation ✔
• 2.3 Exploratory data analysis ✔
• 2.4 Setting up the validation framework ✔
• 2.5 Linear regression ✔
• 2.6 Linear regression: vector form ✔
• 2.7 Training linear regression: Normal equation ✔
• 2.8 Baseline model for car price prediction project ✔
• 2.9 Root mean squared error ✔
• 2.10 Using RMSE on validation data ✔
• 2.11 Feature engineering ✔
• 2.12 Categorical variables ✔
• 2.13 Regularization ✔
• 2.14 Tuning the model ✔
• 2.15 Using the model ✔
• 2.16 Car price prediction project summary ✔

3. Machine Learning for Classification

• 3.1 Churn prediction project ✔
• 3.2 Data preparation ✔
• 3.3 Setting up the validation framework ✔
• 3.4 EDA ✔
• 3.5 Feature importance: Churn rate and risk ratio ✔
• 3.6 Feature importance: Mutual information ✔
• 3.7 Feature importance: Correlation ✔
• 3.8 One-hot encoding ✔
• 3.9 Logistic regression ✔
• 3.10 Training logistic regression with Scikit-Learn ✔
• 3.11 Model interpretation ✔
• 3.12 Using the model ✔

4. Evaluation Metrics for Classification

• 4.1 Evaluation metrics: session overview ✔
• 4.2 Accuracy and dummy model ✔
• 4.3 Confusion table ✔
• 4.4 Precision and Recall ✔
• 4.5 ROC Curves ✔
• 4.6 ROC AUC ✔
• 4.7 Cross-Validation ✔

5. Deploying Machine Learning Models

• 5.1 Intro / Session overview (Fri, 10 Feb 2023)
• 5.2 Saving and loading the model (Fri, 10 Feb 2023)
• 5.3 Web services : Flask (Fri, 10 Feb 2023)
• 5.4 Serving the churn model with Flask (Fri, 10 Feb 2023)
• 5.5 Python virtual environment: Pipenv (Fri, 10 Feb 2023)
• 5.6 Environment management: Docker (Fri, 10 Feb 2023)
• 5.7 Deployment to the cloud: AWS Elastic Beanstalk (optional) (Fri, 10 Feb 2023)

6. Decision Trees and Ensemble Learning

• 6.1 Credit risk scoring project
• 6.2 Data cleaning and preparation
• 6.3 Decision trees
• 6.4 Decision tree learning algorithm
• 6.5 Decision trees parameter tuning
• 6.6 Ensemble learning and random forest
• 6.7 Gradient boosting and XGBoost
• 6.8 XGBoost parameter tuning
• 6.9 Selecting the best model

7. Production-Ready Machine Learning (Bento ML)

• 7.1 Intro/Session Overview
• 7.2 Building Your Prediction Service with BentoML
• 7.3 Deploying Your Prediction Service
• 7.4 Sending, Receiving and Validating Data
• 7.5 High-Performance Serving
• 7.6 Bento Production Deployment

8. Neural Networks and Deep Learning

• 8.1 Fashion classification
• 8.2 TensorFlow and Keras
• 8.3 Pre-trained convolutional neural networks
• 8.4 Convolutional neural networks
• 8.5 Transfer learning
• 8.6 Adjusting the learning rate
• 8.7 Checkpointing
• 8.8 Adding more layers
• 8.9 Regularization and dropout
• 8.10 Data augmentation
• 8.11 Training a larger model
• 8.12 Using the model

9. Serverless Deep Learning

• 9.1 Introduction to Serverless
• 9.2 AWS Lambda
• 9.3 TensorFlow Lite
• 9.4 Preparing the code for Lambda
• 9.5 Preparing a Docker image
• 9.6 Creating the lambda function
• 9.7 API Gateway: exposing the lambda function

10. Kubernetes and Tensorflow Serving

• 10.1 Overview
• 10.2 TensorFlow Serving
• 10.3 Creating a pre-processing service
• 10.4 Running everything locally with Docker-compose
• 10.5 Introduction to Kubernetes
• 10.6 Deploying a simple service to Kubernetes
• 10.7 Deploying TensorFlow models to Kubernetes
• 10.8 Deploying to EKS