Module 1 
Section 1: Welcome to Machine Learning AZ 

Unit 1 
Welcome to the course! 
Unit 2 
Applications of Machine Learning 
Unit 3 
Why Machine Learning is the Future 
Unit 4 
Installing R and R Studio (MAC & Windows) 
Unit 5 
Installing Python and Anaconda (MAC & Windows) 
Module 2 
Section 2:  Part 1  Data Processing  

Unit 1 
Welcome to Part 1  Data Preprocessing 
Unit 2 
Get the dataset 
Unit 3 
Importing the Libraries 
Unit 4 
Importing the Dataset 
Unit 5 
Missing Data 
Unit 6 
Categorical Data 
Unit 7 
Splitting the Dataset into the Training set and Test set 
Unit 8 
Feature Scaling 
Unit 9 
And here is our Data Preprocessing Template ! 
Unit 10 
Quiz 1  Data Preprocessing 
Module 3 
Section 3:  Part 2  Regression  

Unit 1 
Welcome to Part 2  Regression 
Module 4 
Section 4: Simple Linear Regression 

Unit 1 
How to get the dataset 
Unit 2 
Dataset + Business Problem Description 
Unit 3 
Simple Linear Regression Intuition  Step 1 
Unit 4 
Simple Linear Regression Intuition  Step 2 
Unit 5 
Simple Linear Regression in Python  Step 1 
Unit 6 
Simple Linear Regression in Python  Step 2 
Unit 7 
Simple Linear Regression in Python  Step 3 
Unit 8 
Simple Linear Regression in Python  Step 4 
Unit 9 
Simple Linear Regression in R  Step 1 
Unit 10 
Simple Linear Regression in R  Step 2 
Unit 11 
Simple Linear Regression in R  Step 3 
Unit 12 
Simple Linear Regression in R  Step 4 
Unit 13 
Quiz 2  Simple Linear Regression 
Module 5 
Section 5: Multiple Linear Regression 

Unit 1 
How to get the dataset 
Unit 2 
Dataset + Business Problem Description 
Unit 3 
Multiple Linear Regression Intuition  Step 1 
Unit 4 
Multiple Linear Regression Intuition  Step 2 
Unit 5 
Multiple Linear Regression Intuition  Step 3 
Unit 6 
Multiple Linear Regression Intuition  Step 4 
Unit 7 
Multiple Linear Regression Intuition  Step 5 
Unit 8 
Multiple Linear Regression in Python  Step 1 
Unit 9 
Multiple Linear Regression in Python  Step 2 
Unit 10 
Multiple Linear Regression in Python  Step 3 
Unit 11 
Multiple Linear Regression in Python  Backward Elimination  Preparation 
Unit 12 
Multiple Linear Regression in Python  Backward Elimination  HOMEWORK ! 
Unit 13 
Multiple Linear Regression in Python  Backward Elimination  Homework Solution 
Unit 14 
Multiple Linear Regression in R  Step 1 
Unit 15 
Multiple Linear Regression in R  Step 2 
Unit 16 
Multiple Linear Regression in R  Step 3 
Unit 17 
Multiple Linear Regression in R  Backward Elimination  HOMEWORK ! 
Unit 18 
Multiple Linear Regression in R  Backward Elimination  Homework Solution 
Unit 19 
Quiz 3  Multiple Linear Regression 
Module 6 
Section 6: Polynomial Regression 

Unit 1 
Polynomial Regression Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Polynomial Regression in Python  Step 1 
Unit 4 
Polynomial Regression in Python  Step 2 
Unit 5 
Polynomial Regression in Python  Step 3 
Unit 6 
Polynomial Regression in Python  Step 4 
Unit 7 
Python Regression Template 
Unit 8 
Polynomial Regression in R  Step 1 
Unit 9 
Polynomial Regression in R  Step 2 
Unit 10 
Polynomial Regression in R  Step 3 
Unit 11 
Polynomial Regression in R  Step 4 
Unit 12 
R Regression Template 
Module 7 
Section 7: Support Vector Regression (SVR) 

Unit 1 
How to get the dataset 
Unit 2 
SVR in Python 
Unit 3 
SVR in R 
Module 8 
Section 8: Decision Tree Regression 

Unit 1 
Decision Tree Regression Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Random Forest Regression in Python 
Unit 4 
Decision Tree Regression in R 
Module 9 
Section 9: Random Forest Regression 

Unit 1 
Random Forest Regression Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Random Forest Regression in Python 
Unit 4 
Random Forest Regression in R 
Module 10 
Section 10: Evaluating Regression Models Performance 

Unit 1 
RSquared Intuition 
Unit 2 
Adjusted RSquared Intuition 
Unit 3 
Evaluating Regression Models Performance  Homework's Final Part 
Unit 4 
Interpreting Linear Regression Coefficients 
Module 11 
Section 11:  Part 3: Classification  

Unit 1 
Welcome to Part 3  Classification 
Module 12 
Section 12: Logistic Regression 

Unit 1 
Logistic Regression Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Logistic Regression in Python  Step 1 
Unit 4 
Logistic Regression in Python  Step 2 
Unit 5 
Logistic Regression in Python  Step 3 
Unit 6 
Logistic Regression in Python  Step 4 
Unit 7 
Logistic Regression in Python  Step 5 
Unit 8 
Python Classification Template 
Unit 9 
Logistic Regression in R  Step 1 
Unit 10 
Logistic Regression in R  Step 2 
Unit 11 
Logistic Regression in R  Step 3 
Unit 12 
Logistic Regression in R  Step 4 
Unit 13 
Logistic Regression in R  Step 5 
Unit 14 
R Classification Template 
Unit 15 
Quiz 4 – Logistic Regression 
Module 13 
Section 13: kNearest Neighbors (kNN) 

Unit 1 
KNearest Neighbor Intuition 
Unit 2 
How to get the dataset 
Unit 3 
KNN in Python 
Unit 4 
KNN in R 
Unit 5 
Quiz 5  KNearest Neighbor 
Module 14 
Section 14: Support Vector Machine (SVM) 

Unit 1 
SVM Intuition 
Unit 2 
How to get the dataset 
Unit 3 
SVM in Python 
Unit 4 
SVM in R 
Module 15 
Section 15: Kernel SVM 

Unit 1 
Kernel SVM Intuition 
Unit 2 
Mapping to a higher dimension 
Unit 3 
The Kernel Trick 
Unit 4 
Types of Kernel Functions 
Unit 5 
How to get the dataset 
Unit 6 
Kernel SVM in Python 
Unit 7 
Kernel SVM in R 
Module 16 
Section 16: Naive Bayes 

Unit 1 
Bayes Theorem 
Unit 2 
Naive Bayes Intuition 
Unit 3 
Naive Bayes Intuition (Challenge Reveal) 
Unit 4 
Naive Bayes Intuition (Extras) 
Unit 5 
How to get the dataset 
Unit 6 
Naive Bayes in Python 
Unit 7 
Naive Bayes in R 
Module 17 
Section 17: Decision Tree Classification 

Unit 1 
Decision Tree Classification Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Decision Tree Classification in Python 
Unit 4 
Decision Tree Classification in R 
Module 18 
Section 18: Random Forest Classification 

Unit 1 
Random Forest Classification Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Random Forest Classification in Python 
Unit 4 
Random Forest Classification in R 
Module 19 
Section 19: Evaluating Classification Models Performance 

Unit 1 
False Positives & False Negatives 
Unit 2 
Confusion Matrix 
Unit 3 
Accuracy Paradox 
Unit 4 
CAP Curve 
Unit 5 
CAP Curve Analysis 
Module 20 
Section 20:  Part 4: Clustering  

Unit 1 
Welcome to Part 4  Clustering 
Module 21 
Section 21: KMeans Clustering 

Unit 1 
KMeans Clustering Intuition 
Unit 2 
KMeans Random Initialization Trap 
Unit 3 
KMeans Selecting The Number Of Clusters 
Unit 4 
KMeans Clustering in Python 
Unit 5 
How to get the dataset 
Unit 6 
KMeans Clustering in R 
Unit 7 
Quiz 6  KMeans Clustering 
Module 22 
Section 22: Hierarchical Clustering 

Unit 1 
Hierarchical Clustering Intuition 
Unit 2 
Hierarchical Clustering How Dendrograms Work 
Unit 3 
Hierarchical Clustering Using Dendrograms 
Unit 4 
How to get the dataset 
Unit 5 
HC in Python  Step 1 
Unit 6 
HC in Python  Step 2 
Unit 7 
HC in Python  Step 3 
Unit 8 
HC in Python  Step 4 
Unit 9 
HC in Python  Step 5 
Unit 10 
HC in R  Step 1 
Unit 11 
HC in R  Step 2 
Unit 12 
HC in R  Step 3 
Unit 13 
HC in R  Step 4 
Unit 14 
HC in R  Step 5 
Unit 15 
Quiz 7  Hierarchical Clustering 
Module 23 
Section 23:  Part 5: Association Rule Learning  

Unit 1 
Welcome to Part 5  Association Rule Learning 
Module 24 
Section 24: Apriori 

Unit 1 
Apriori Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Apriori in R  Step 1 
Unit 4 
Apriori in R  Step 2 
Unit 5 
Apriori in R  Step 3 
Unit 6 
Apriori in Python  Step 1 
Unit 7 
Apriori in Python  Step 2 
Unit 8 
Apriori in Python  Step 3 
Module 25 
Section 25: Eclat 

Unit 1 
Eclat Intuition 
Unit 2 
How to get the dataset 
Unit 3 
Eclat in R 
Module 26 
Section 26:  Part 6: Reinforcement Learning  

Unit 1 
Welcome to Part 6  Reinforcement Learning 
Module 27 
Section 27: Upper Confidence Bound (UCB) 

Unit 1 
The MultiArmed Bandit Problem 
Unit 2 
Upper Confidence Bound (UCB) Intuition 
Unit 3 
How to get the dataset 
Unit 4 
Upper Confidence Bound in Python  Step 1 
Unit 5 
Upper Confidence Bound in Python  Step 2 
Unit 6 
Upper Confidence Bound in Python  Step 3 
Unit 7 
Upper Confidence Bound in Python  Step 4 
Unit 8 
Upper Confidence Bound in R  Step 1 
Unit 9 
Upper Confidence Bound in R  Step 2 
Unit 10 
Upper Confidence Bound in R  Step 3 
Unit 11 
Upper Confidence Bound in R  Step 4 
Module 28 
Section 28: Thompson Sampling 

Unit 1 
Thompson Sampling Intuition 
Unit 2 
Algorithm Comparison: UCB vs Thompson Sampling 
Unit 3 
How to get the dataset 
Unit 4 
Thompson Sampling in Python  Step 1 
Unit 5 
Thompson Sampling in Python  Step 2 
Unit 6 
Thompson Sampling in R  Step 1 
Unit 7 
Thompson Sampling in R  Step 2 
Module 29 
Section 29:  Part 7: Natural Language Processing  

Unit 1 
Welcome to Part 7  Natural Language Processing 
Unit 2 
How to get the dataset 
Unit 3 
Natural Language Processing in Python  Step 1 
Unit 4 
Natural Language Processing in Python  Step 2 
Unit 5 
Natural Language Processing in Python  Step 3 
Unit 6 
Natural Language Processing in Python  Step 4 
Unit 7 
Natural Language Processing in Python  Step 5 
Unit 8 
Natural Language Processing in Python  Step 6 
Unit 9 
Natural Language Processing in Python  Step 7 
Unit 10 
Natural Language Processing in Python  Step 8 
Unit 11 
Natural Language Processing in Python  Step 9 
Unit 12 
Natural Language Processing in Python  Step 10 
Unit 13 
Homework Challenge 
Unit 14 
Natural Language Processing in R  Step 1 
Unit 15 
Natural Language Processing in R  Step 2 
Unit 16 
Natural Language Processing in R  Step 3 
Unit 17 
Natural Language Processing in R  Step 4 
Unit 18 
Natural Language Processing in R  Step 5 
Unit 19 
Natural Language Processing in R  Step 6 
Unit 20 
Natural Language Processing in R  Step 7 
Unit 21 
Natural Language Processing in R  Step 8 
Unit 22 
Natural Language Processing in R  Step 9 
Unit 23 
Natural Language Processing in R  Step 10 
Module 30 
Section 30: Part 8  Deep Learning  

Unit 1 
Welcome to Part 8  Deep Learning 
Unit 2 
What is Deep Learning? 
Module 31 
Section 31: Artificial Neural Networks (ANN) 

Unit 1 
Plan of attack 
Unit 2 
The Neuron 
Unit 3 
The Activation Function 
Unit 4 
How do Neural Networks work? 
Unit 5 
How do Neural Networks learn? 
Unit 6 
Gradient Descent 
Unit 7 
Stochastic Gradient Descent 
Unit 8 
Back Propagation 
Unit 9 
How to get the dataset 
Unit 10 
Business Problem Description 
Unit 11 
ANN in Python  Step 1  Installing Theano, Tensorflow and Keras 
Unit 12 
ANN in Python  Step 2 
Unit 13 
ANN in Python  Step 3 
Unit 14 
ANN in Python  Step 4 
Unit 15 
ANN in Python  Step 5 
Unit 16 
ANN in Python  Step 6 
Unit 17 
ANN in Python  Step 7 
Unit 18 
ANN in Python  Step 8 
Unit 19 
ANN in Python  Step 9 
Unit 20 
ANN in Python  Step 10 
Unit 21 
ANN in R  Step 1 
Unit 22 
ann in R 2 
Unit 23 
ANN in R  Step 3 
Unit 24 
ANN in R  Step 4 (Last step) 
Module 32 
Section 32: Convolutional Neural Networks (CNN) 

Unit 1 
Plan of attack 
Unit 2 
What are convolutional neural networks? 
Unit 3 
Step 1  Convolution Operation 
Unit 4 
Step 1(b)  ReLU Layer 
Unit 5 
Step 2  Pooling 
Unit 6 
Step 3  Flattening 
Unit 7 
Step 4  Full Connection 
Unit 8 
Summary 
Unit 9 
Softmax & CrossEntropy 
Unit 10 
How to get the dataset 
Unit 11 
CNN in Python  Step 1 
Unit 12 
CNN in Python  Step 2 
Unit 13 
CNN in Python  Step 3 
Unit 14 
CNN in Python  Step 4 
Unit 15 
CNN in Python  Step 5 
Unit 16 
CNN in Python  Step 6 
Unit 17 
CNN in Python  Step 7 
Unit 18 
CNN in Python  Step 8 
Unit 19 
CNN in Python  Step 9 
Unit 20 
CNN in Python  Step 10 
Module 33 
Section 33:  Part 9: Dimensionality Reduction  

Unit 1 
Welcome to Part 9  Dimensionality Reduction 
Module 34 
Section 34: Principal Component Analysis (PCA) 

Unit 1 
How to get the dataset 
Unit 2 
PCA in Python  Step 1 
Unit 3 
PCA in Python  Step 2 
Unit 4 
PCA in Python  Step 3 
Unit 5 
PCA in R  Step 1 
Unit 6 
PCA in R  Step 2 
Unit 7 
PCA in R  Step 3 
Module 35 
Section 35: Linear Discriminant Analysis (LDA) 

Unit 1 
How to get the dataset 
Unit 2 
LDA in Python 
Unit 3 
LDA in R 
Module 36 
Section 36: Kernel PCA 

Unit 1 
How to get the dataset 
Unit 2 
Kernel PCA in Python 
Unit 3 
Kernel PCA in R 
Module 37 
Section 37:  Part 10  Model Selection & Boosting  

Unit 1 
Welcome to Part 10  Model Selection & Boosting 
Module 38 
Section 38: Model Selection 

Unit 1 
How to get the dataset 
Unit 2 
kFold Cross Validation in Python 
Unit 3 
kFold Cross Validation in R 
Unit 4 
Grid Search in Python  Step 1 
Unit 5 
Grid Search in Python  Step 2 
Unit 6 
Grid Search in R 
Module 39 
Section 39  XGBoost 

Unit 1 
How to get the dataset 
Unit 2 
XGBoost in Python  Step 1 
Unit 3 
XGBoost in Python  Step 2 
Unit 4 
XGBoost in R 