GitHub

Exercises location

Notes

Feature scaling should be applied after the train/test data split to prevent data leakage from a validation set to a train set.
No need to check for [linear regression assumptions] prior to trying out linear regression model. If some of the data doesn't have linear correlation, an LR model will just perform poorly compared to other models that we should try anyways.
When presented with a task and a dataset, how do I choose which type of regression start applying?

– Models of different types need to be tested and compared in terms of accuracy

Course Curriculum

Section 1: Welcome to the course! Here we will help you get started

                in the best conditions.

Welcome Challenge!
Machine Learning Demo - Get Excited!
Get all the Datasets, Codes and Slides here
How to use the ML A-Z folder Google Colab
Installing R and R Studio (Mac, Linux Windows)
BONUS: Use ChatGPT to Boost your ML Skills

Section 2: Part 1: Data Preprocessing

Welcome to Part 1 - Data Preprocessing
The Machine Learning process
Splitting the data into a Training and Test set
Feature Scaling

Section 3: Data Preprocessing in Python

Getting Started - Step 1
Getting Started - Step 2
Importing the Libraries
Importing the Dataset - Step 1
Importing the Dataset - Step 2
Importing the Dataset - Step 3
For Python learners, summary of Object-oriented programming: classes objects Coding Exercise 1: Coding Exercise 1: Importing and Preprocessing a Dataset for Machine Learning
Taking care of Missing Data - Step 1
Taking care of Missing Data - Step 2

Coding Exercise 2: Coding Exercise 2: Handling Missing Data in a Dataset for Machine Learning 20. Encoding Categorical Data - Step 1

Encoding Categorical Data - Step 2
Encoding Categorical Data - Step 3

Coding Exercise 3: Coding Exercise 3: Encoding Categorical Data for Machine Learning 23. Splitting the dataset into the Training set and Test set - Step 1 24. Splitting the dataset into the Training set and Test set - Step 2 25. Splitting the dataset into the Training set and Test set - Step 3 Coding Exercise 4: Coding Exercise 4: Dataset Splitting and Feature Scaling 26. Feature Scaling - Step 1

Feature Scaling - Step 2
Feature Scaling - Step 3
Feature Scaling - Step 4

Coding Exercise 5: Coding exercise 5: Feature scaling for Machine Learning

Section 4: Data Preprocessing in R

Getting Started
Dataset Description
Importing the Dataset
Taking care of Missing Data
Encoding Categorical Data
Splitting the dataset into the Training set and Test set - Step 1
Splitting the dataset into the Training set and Test set - Step 2
Feature Scaling - Step 1
Feature Scaling - Step 2
Data Preprocessing Template

Quiz 1: Data Preprocessing Quiz

Section 5: Part 2: Regression

Welcome to Part 2 - Regression

Section 6: Simple Linear Regression

Simple Linear Regression Intuition
Ordinary Least Squares
Simple Linear Regression in Python - Step 1a
Simple Linear Regression in Python - Step 1b
Simple Linear Regression in Python - Step 2a
Simple Linear Regression in Python - Step 2b
Simple Linear Regression in Python - Step 3
Simple Linear Regression in Python - Step 4a
Simple Linear Regression in Python - Step 4b
Simple Linear Regression in Python - Additional Lecture
Simple Linear Regression in R - Step 1
Simple Linear Regression in R - Step 2
Simple Linear Regression in R - Step 3
Simple Linear Regression in R - Step 4a
Simple Linear Regression in R - Step 4b
Simple Linear Regression in R - Step 4c

Quiz 2: Simple Linear Regression Quiz

Section 7: Multiple Linear Regression

Dataset + Business Problem Description
Multiple Linear Regression Intuition
Assumptions of Linear Regression
Multiple Linear Regression Intuition - Step 3
Multiple Linear Regression Intuition - Step 4
Understanding the P-Value
Multiple Linear Regression Intuition - Step 5
Multiple Linear Regression in Python - Step 1a
Multiple Linear Regression in Python - Step 1b
Multiple Linear Regression in Python - Step 2a
Multiple Linear Regression in Python - Step 2b
Multiple Linear Regression in Python - Step 3a
Multiple Linear Regression in Python - Step 3b
Multiple Linear Regression in Python - Step 4a
Multiple Linear Regression in Python - Step 4b
Multiple Linear Regression in Python - Backward Elimination
Multiple Linear Regression in Python - EXTRA CONTENT
Multiple Linear Regression in R - Step 1a
Multiple Linear Regression in R - Step 1b
Multiple Linear Regression in R - Step 2a
Multiple Linear Regression in R - Step 2b
Multiple Linear Regression in R - Step 3
Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
Multiple Linear Regression in R - Backward Elimination - Homework Solution
Multiple Linear Regression in R - Automatic Backward Elimination Quiz 3: Multiple Linear Regression Quiz

Section 8: Polynomial Regression

Polynomial Regression Intuition
Polynomial Regression in Python - Step 1a
Polynomial Regression in Python - Step 1b
Polynomial Regression in Python - Step 2a
Polynomial Regression in Python - Step 2b
Polynomial Regression in Python - Step 3a
Polynomial Regression in Python - Step 3b
Polynomial Regression in Python - Step 4a
Polynomial Regression in Python - Step 4b
Polynomial Regression in R - Step 1a
Polynomial Regression in R - Step 1b
Polynomial Regression in R - Step 2a
Polynomial Regression in R - Step 2b
Polynomial Regression in R - Step 3a
Polynomial Regression in R - Step 3b
Polynomial Regression in R - Step 3c
Polynomial Regression in R - Step 4a
Polynomial Regression in R - Step 4b
R Regression Template - Step 1
R Regression Template - Step 2

Quiz 4: Polynomial Regression Quiz

Section 9: Support Vector Regression (SVR)

SVR Intuition (Updated!)
Heads-up on non-linear SVR
SVR in Python - Step 1a
SVR in Python - Step 1b
SVR in Python - Step 2a
SVR in Python - Step 2b
SVR in Python - Step 2c
SVR in Python - Step 3
SVR in Python - Step 4
SVR in Python - Step 5a
SVR in Python - Step 5b
SVR in R - Step 1
SVR in R - Step 2

Quiz 5: SVR Quiz

Section 10: Decision Tree Regression

Decision Tree Regression Intuition
Decision Tree Regression in Python - Step 1a
Decision Tree Regression in Python - Step 1b
Decision Tree Regression in Python - Step 2
Decision Tree Regression in Python - Step 3
Decision Tree Regression in Python - Step 4
Decision Tree Regression in R - Step 1
Decision Tree Regression in R - Step 2
Decision Tree Regression in R - Step 3
Decision Tree Regression in R - Step 4

Quiz 6: Decision Tree Regression Quiz

Section 11: Random Forest Regression

Random Forest Regression Intuition
Random Forest Regression in Python - Step 1
Random Forest Regression in Python - Step 2
Random Forest Regression in R - Step 1
Random Forest Regression in R - Step 2
Random Forest Regression in R - Step 3

Quiz 7: Random Forest Regression Quiz

Section 12: Evaluating Regression Models

                Performance

R-Squared Intuition
Adjusted R-Squared Intuition

Quiz 8: Evaluating Regression Models Performance Quiz

Section 13: Regression Model Selection in

                Python

Make sure you have this Model Selection folder ready
Preparation of the Regression Code Templates - Step 1
Preparation of the Regression Code Templates - Step 2
Preparation of the Regression Code Templates - Step 3
Preparation of the Regression Code Templates - Step 4
THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 1
THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 2
Conclusion of Part 2 - Regression

Section 14: Regression Model Selection in R

Evaluating Regression Models Performance - Homework's Final Part
Interpreting Linear Regression Coefficients
Conclusion of Part 2 - Regression

Section 15: Part 3: Classification

Welcome to Part 3 - Classification
What is Classification?

Section 16: Logistic Regression

Logistic Regression Intuition
Maximum Likelihood
Logistic Regression in Python - Step 1a
Logistic Regression in Python - Step 1b
Logistic Regression in Python - Step 2a
Logistic Regression in Python - Step 2b
Logistic Regression in Python - Step 3a
Logistic Regression in Python - Step 3b
Logistic Regression in Python - Step 4a
Logistic Regression in Python - Step 4b
Logistic Regression in Python - Step 5
Logistic Regression in Python - Step 6a
Logistic Regression in Python - Step 6b
Logistic Regression in Python - Step 7a
Logistic Regression in Python - Step 7b
Logistic Regression in Python - Step 7c
Logistic Regression in Python - Step 7 (Colour-blind friendly image)
Logistic Regression in R - Step 1
Logistic Regression in R - Step 2
Logistic Regression in R - Step 3
Logistic Regression in R - Step 4
Warning - Update
Logistic Regression in R - Step 5a
Logistic Regression in R - Step 5b
Logistic Regression in R - Step 5c
Logistic Regression in R - Step 5 (Colour-blind friendly image)
R Classification Template
Machine Learning Regression and Classification BONUS Quiz 9: Logistic Regression Quiz
EXTRA CONTENT: Logistic Regression Practical Case Study

Section 17: K-Nearest Neighbors (K-NN)

K-Nearest Neighbor Intuition
K-NN in Python - Step 1
K-NN in Python - Step 2
K-NN in Python - Step 3
K-NN in R - Step 1
K-NN in R - Step 2
K-NN in R - Step 3

Quiz 10: K-Nearest Neighbor Quiz

Section 18: Support Vector Machine (SVM)

SVM Intuition
SVM in Python - Step 1
SVM in Python - Step 2
SVM in Python - Step 3
SVM in R - Step 1
SVM in R - Step 2

Quiz 11: SVM Quiz

Section 19: Kernel SVM

Kernel SVM Intuition
Mapping to a higher dimension
The Kernel Trick
Types of Kernel Functions
Non-Linear Kernel SVR (Advanced)
Kernel SVM in Python - Step 1
Kernel SVM in Python - Step 2
Kernel SVM in R - Step 1
Kernel SVM in R - Step 2
Kernel SVM in R - Step 3

Quiz 12: Kernel SVM Quiz

Section 20: Naive Bayes

Bayes Theorem
Naive Bayes Intuition
Naive Bayes Intuition (Challenge Reveal)
Naive Bayes Intuition (Extras)
Naive Bayes in Python - Step 1
Naive Bayes in Python - Step 2
Naive Bayes in Python - Step 3
Naive Bayes in R - Step 1
Naive Bayes in R - Step 2
Naive Bayes in R - Step 3

Quiz 13: Naive Bayes Quiz

Section 21: Decision Tree Classification

Decision Tree Classification Intuition
Decision Tree Classification in Python - Step 1
Decision Tree Classification in Python - Step 2
Decision Tree Classification in R - Step 1
Decision Tree Classification in R - Step 2
Decision Tree Classification in R - Step 3

Quiz 14: Decision Tree Classification Quiz

Section 22: Random Forest Classification

Random Forest Classification Intuition
Random Forest Classification in Python - Step 1
Random Forest Classification in Python - Step 2
Random Forest Classification in R - Step 1
Random Forest Classification in R - Step 2
Random Forest Classification in R - Step 3

Quiz 15: Random Forest Classification Quiz

Section 23: Classification Model Selection in

                Python

Make sure you have this Model Selection folder ready
Confusion Matrix Accuracy Ratios
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 3
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 4

Section 24: Evaluating Classification Models

                Performance

False Positives False Negatives
Accuracy Paradox
CAP Curve
CAP Curve Analysis
Conclusion of Part 3 - Classification

Quiz 16: Evaluating Classiification Model Performance Quiz

Section 25: Part 4: Clustering

Welcome to Part 4 - Clustering

Section 26: K-Means Clustering

What is Clustering? (Supervised vs Unsupervised Learning)
K-Means Clustering Intuition
The Elbow Method
K-Means++
K-Means Clustering in Python - Step 1a
K-Means Clustering in Python - Step 1b
K-Means Clustering in Python - Step 2a
K-Means Clustering in Python - Step 2b
K-Means Clustering in Python - Step 3a
K-Means Clustering in Python - Step 3b
K-Means Clustering in Python - Step 3c
K-Means Clustering in Python - Step 4
K-Means Clustering in Python - Step 5a
K-Means Clustering in Python - Step 5b
K-Means Clustering in Python - Step 5c
K-Means Clustering in R - Step 1
K-Means Clustering in R - Step 2

Quiz 17: K-Means Clustering Quiz

Section 27: Hierarchical Clustering

Hierarchical Clustering Intuition
Hierarchical Clustering How Dendrograms Work
Hierarchical Clustering Using Dendrograms
Hierarchical Clustering in Python - Step 1
Hierarchical Clustering in Python - Step 2a
Hierarchical Clustering in Python - Step 2b
Hierarchical Clustering in Python - Step 2c
Hierarchical Clustering in Python - Step 3a
Hierarchical Clustering in Python - Step 3b
Hierarchical Clustering in R - Step 1
Hierarchical Clustering in R - Step 2
Hierarchical Clustering in R - Step 3
Hierarchical Clustering in R - Step 4
Hierarchical Clustering in R - Step 5

Quiz 18: Hierarchical Clustering Quiz

Conclusion of Part 4 - Clustering

Section 28: Part 5: Association Rule Learning

Welcome to Part 5 - Association Rule Learning

Section 29: Apriori

Apriori Intuition
Apriori in Python - Step 1
Apriori in Python - Step 2
Apriori in Python - Step 3
Apriori in Python - Step 4
Apriori in R - Step 1
Apriori in R - Step 2
Apriori in R - Step 3

Quiz 19: Apriori Quiz

Section 30: Eclat

Eclat Intuition
Eclat in Python
Eclat in R

Quiz 20: Eclat Quiz

Section 31: Part 6: Reinforcement Learning

Welcome to Part 6 - Reinforcement Learning

Section 32: Upper Confidence Bound (UCB)

The Multi-Armed Bandit Problem
Upper Confidence Bound (UCB) Intuition
Upper Confidence Bound in Python - Step 1
Upper Confidence Bound in Python - Step 2
Upper Confidence Bound in Python - Step 3
Upper Confidence Bound in Python - Step 4
Upper Confidence Bound in Python - Step 5
Upper Confidence Bound in Python - Step 6
Upper Confidence Bound in Python - Step 7
Upper Confidence Bound in R - Step 1
Upper Confidence Bound in R - Step 2
Upper Confidence Bound in R - Step 3
Upper Confidence Bound in R - Step 4

Quiz 21: Upper Confidence Bound Quiz

Section 33: Thompson Sampling

Thompson Sampling Intuition
Algorithm Comparison: UCB vs Thompson Sampling
Thompson Sampling in Python - Step 1
Thompson Sampling in Python - Step 2
Thompson Sampling in Python - Step 3
Thompson Sampling in Python - Step 4
Additional Resource for this Section
Thompson Sampling in R - Step 1
Thompson Sampling in R - Step 2

Quiz 22: Thompson Sampling Quiz

Section 34: Part 7: Natural Language Processing

Welcome to Part 7 - Natural Language Processing
NLP Intuition
Types of Natural Language Processing
Classical vs Deep Learning Models
Bag-Of-Words Model
Natural Language Processing in Python - Step 1
Natural Language Processing in Python - Step 2
Natural Language Processing in Python - Step 3
Natural Language Processing in Python - Step 4
Natural Language Processing in Python - Step 5
Natural Language Processing in Python - Step 6
Natural Language Processing in Python - BONUS
Homework Challenge
Natural Language Processing in R - Step 1
Warning - Update
Natural Language Processing in R - Step 2
Natural Language Processing in R - Step 3
Natural Language Processing in R - Step 4
Natural Language Processing in R - Step 5
Natural Language Processing in R - Step 6
Natural Language Processing in R - Step 7
Natural Language Processing in R - Step 8
Natural Language Processing in R - Step 9
Natural Language Processing in R - Step 10
Homework Challenge

Quiz 23: Natural Language Processing Quiz

Section 35: Part 8: Deep Learning

Welcome to Part 8 - Deep Learning
What is Deep Learning?

Quiz 24: Deep Learning Quiz

Section 36: Artificial Neural Networks

Plan of attack
The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Backpropagation
Business Problem Description
ANN in Python - Step 1
ANN in Python - Step 2
ANN in Python - Step 3
ANN in Python - Step 4
ANN in Python - Step 5
ANN in R - Step 1
ANN in R - Step 2
ANN in R - Step 3
ANN in R - Step 4 (Last step)
Deep Learning Additional Content
EXTRA CONTENT: ANN Case Study

Quiz 25: ANN QUIZ

Section 37: Convolutional Neural Networks

Plan of attack
What are convolutional neural networks?
Step 1 - Convolution Operation
Step 1(b) - ReLU Layer
Step 2 - Pooling
Step 3 - Flattening
Step 4 - Full Connection
Summary
Softmax Cross-Entropy
CNN in Python - Step 1
CNN in Python - Step 2
CNN in Python - Step 3
CNN in Python - Step 4
CNN in Python - Step 5
CNN in Python - FINAL DEMO!
Deep Learning Additional Content #2

Quiz 26: CNN Quiz

Section 38: Part 9: Dimensionality Reduction

Welcome to Part 9 - Dimensionality Reduction

Section 39: Principal Component Analysis (PCA)

Principal Component Analysis (PCA) Intuition
PCA in Python - Step 1
PCA in Python - Step 2
PCA in R - Step 1
PCA in R - Step 2
PCA in R - Step 3

Quiz 27: PCA Quiz

Section 40: Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA) Intuition
LDA in Python
LDA in R

Quiz 28: LDA Quiz

Section 41: Kernel PCA

Kernel PCA in Python
Kernel PCA in R

Section 42: Part 10: Model Selection

                Boosting

Welcome to Part 10 - Model Selection Boosting

Section 43: Model Selection

k-Fold Cross Validation in Python
Grid Search in Python
k-Fold Cross Validation in R
Grid Search in R

Section 44: XGBoost

XGBoost in Python
Model Selection and Boosting Additional Content
XGBoost in R

Section 45: Annex: Logistic Regression (Long

                Explanation)

Logistic Regression Intuition

Name		Name	Last commit message	Last commit date
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src		src
.gitignore		.gitignore
Classification_Pros_Cons.pdf		Classification_Pros_Cons.pdf
Linear-Regression-Assumptions.pdf		Linear-Regression-Assumptions.pdf
README.md		README.md
[lrnn]-Data-science-udemy-machine-learninig.md.scm		[lrnn]-Data-science-udemy-machine-learninig.md.scm
coding_ai_best_practices.ipynb		coding_ai_best_practices.ipynb
coding_ai_best_practices.py		coding_ai_best_practices.py
regression-models-general-pros-cons.png		regression-models-general-pros-cons.png
requirements.txt		requirements.txt

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