Module 1 |
Section 1: Welcome to Machine Learning A-Z |

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 ---------------------- |

There are no units in this module. |

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 |
R-Squared Intuition |

Unit 2 |
Adjusted R-Squared Intuition |

Unit 3 |
Evaluating Regression Models Performance - Homework's Final Part |

Unit 4 |
Interpreting Linear Regression Coefficients |

Module 11 |
Section 11: Regularization Methods |

There are no units in this module. |

Module 12 |
Section 12: Part Recap |

There are no units in this module. |

Module 13 |
Section 13: ---------------------- Part 3: Classification ---------------------- |

There are no units in this module. |

Module 14 |
Section 14: 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 15 |
Section 15: k-Nearest Neighbors (k-NN) |

Unit 1 |
K-Nearest Neighbor Intuition |

Unit 2 |
How to get the dataset |

Unit 3 |
K-NN in Python |

Unit 4 |
K-NN in R |

Unit 5 |
Quiz 5 - K-Nearest Neighbor |

Module 16 |
Section 16: 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 17 |
Section 17: 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 18 |
Section 18: 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 19 |
Section 19: 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 20 |
Section 20: 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 21 |
Section 21: 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 22 |
Section 22: Part Recap |

There are no units in this module. |

Module 23 |
Section 23: ---------------------------- Part 4: Clustering ---------------------------- |

There are no units in this module. |

Module 24 |
Section 24: K-Means Clustering |

Unit 1 |
K-Means Clustering Intuition |

Unit 2 |
K-Means Random Initialization Trap |

Unit 3 |
K-Means Selecting The Number Of Clusters |

Unit 4 |
K-Means Clustering in Python |

Unit 5 |
How to get the dataset |

Unit 6 |
K-Means Clustering in R |

Unit 7 |
Quiz 6 - K-Means Clustering |

Module 25 |
Section 25: 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 26 |
Section 26: Part Recap |

There are no units in this module. |

Module 27 |
Section 27: ---------------------------- Part 5: Association Rule Learning ---------------------------- |

There are no units in this module. |

Module 28 |
Section 28: 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 29 |
Section 29: Eclat |

Unit 1 |
Eclat Intuition |

Unit 2 |
How to get the dataset |

Unit 3 |
Eclat in R |

Module 30 |
Section 30 - Part Recap |

There are no units in this module. |

Module 31 |
Section 31: ---------------------------- Part 6: Reinforcement Learning ---------------------------- |

There are no units in this module. |

Module 32 |
Section 32: Upper Confidence Bound (UCB) |

Unit 1 |
The Multi-Armed 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 33 |
Section 33: 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 34 |
Section 34: Part Recap |

There are no units in this module. |

Module 35 |
Section 35: ---------------------------- Part 7: Natural Language Processing ---------------------------- |

There are no units in this module. |

Module 36 |
Section 36: Natural Language Processing |

Unit 1 |
Natural Language Processing in Python - Step 1 |

Unit 2 |
Natural Language Processing in Python - Step 2 |

Unit 3 |
Natural Language Processing in Python - Step 3 |

Unit 4 |
Natural Language Processing in Python - Step 4 |

Unit 5 |
Natural Language Processing in Python - Step 5 |

Unit 6 |
Natural Language Processing in Python - Step 6 |

Unit 7 |
Natural Language Processing in Python - Step 7 |

Unit 8 |
Natural Language Processing in Python - Step 8 |

Unit 9 |
Natural Language Processing in Python - Step 9 |

Unit 10 |
Natural Language Processing in Python - Step 10 |

Unit 11 |
Natural Language Processing in R - Step 1 |

Unit 12 |
Natural Language Processing in R - Step 2 |

Unit 13 |
Natural Language Processing in R - Step 3 |

Unit 14 |
Natural Language Processing in R - Step 4 |

Unit 15 |
Natural Language Processing in R - Step 5 |

Unit 16 |
Natural Language Processing in R - Step 6 |

Unit 17 |
Natural Language Processing in R - Step 7 |

Unit 18 |
Natural Language Processing in R - Step 8 |

Unit 19 |
Natural Language Processing in R - Step 9 |

Unit 20 |
Natural Language Processing in R - Step 10 |

Module 37 |
Section 37: Part Recap |

There are no units in this module. |

Module 38 |
Section 38: ----------------------------Part 8 - Deep Learning ---------------------------- |

Unit 1 |
What is Deep Learning? |

Module 39 |
Section 39: 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 |

Module 40 |
Section 40: Convolutional Neural Networks (CNN) |

Unit 1 |
What are convolutional neural networks? |

Unit 2 |
Step 1 - Convolution Operation |

Unit 3 |
Step 1(b) - ReLU Layer |

Unit 4 |
Step 2 - Pooling |

Unit 5 |
Step 3 - Flattening |

Unit 6 |
Step 4 - Full Connection |

Unit 7 |
Summary |

Unit 8 |
Softmax & Cross-Entropy |

Unit 9 |
How to get the dataset |

Unit 10 |
CNN in Python - Step 1 |

Unit 11 |
CNN in Python - Step 2 |

Unit 12 |
CNN in Python - Step 3 |

Unit 13 |
CNN in Python - Step 4 |

Unit 14 |
CNN in Python - Step 5 |

Unit 15 |
CNN in Python - Step 6 |

Unit 16 |
CNN in Python - Step 7 |

Unit 17 |
CNN in Python - Step 8 |

Unit 18 |
CNN in Python - Step 9 |

Unit 19 |
CNN in Python - Step 10 |

Module 41 |
Section 41: Part Recap |

There are no units in this module. |

Module 42 |
Section 42: ---------------------------- Part 9: Dimensionality Reduction ---------------------------- |

There are no units in this module. |

Module 43 |
Section 43: 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 44 |
Section 44: Linear Discriminant Analysis (LDA) |

Unit 1 |
How to get the dataset |

Unit 2 |
LDA in Python |

Unit 3 |
LDA in R |

Module 45 |
Section 45: Kernel PCA |

Unit 1 |
How to get the dataset |

Unit 2 |
Kernel PCA in Python |

Unit 3 |
Kernel PCA in R |

Module 46 |
Section 46: Part Recap |

There are no units in this module. |

Module 47 |
Section 47: -------------------- Part 10 - Model Selection & Boosting -------------------- |

There are no units in this module. |

Module 48 |
Section 48: Model Selection |

Unit 1 |
How to get the dataset |

Unit 2 |
k-Fold Cross Validation in Python |

Unit 3 |
k-Fold 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 49 |
Section 49 - 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 |