Module 1 |
Section 1: Welcome to the course! |

Unit 1 |
Welcome to the course! |

Unit 2 |
Installing R and R Studio (MAC & Windows) |

Unit 3 |
Installing Python and Anaconda (MAC & Windows) |

Module 2 |
Section 2: -------------------------- Part 1: Data Preprocessing -------------------------- |

Unit 1 |
Welcome to Part 1 Preview |

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 |
Dataset + Business Problem Description |

Unit 2 |
Simple Linear Regression Intuition - Step 1 |

Unit 3 |
Simple Linear Regression Intuition - Step 2 |

Unit 4 |
Simple Linear Regression in Python - Step 1 |

Unit 5 |
Simple Linear Regression in Python - Step 2 |

Unit 6 |
Simple Linear Regression in Python - Step 3 |

Unit 7 |
Simple Linear Regression in Python - Step 4 |

Unit 8 |
Simple Linear Regression in R - Step 1 |

Unit 9 |
Simple Linear Regression in R - Step 2 |

Unit 10 |
Simple Linear Regression in R - Step 3 |

Unit 11 |
Simple Linear Regression in R - Step 4 |

Unit 12 |
Quiz 2 - Simple Linear Regression |

Module 5 |
Section 5: Multiple Linear Regression |

Unit 1 |
Dataset + Business Problem Description |

Unit 2 |
Multiple Linear Regression Intuition - Step 1 |

Unit 3 |
Multiple Linear Regression Intuition - Step 2 |

Unit 4 |
Multiple Linear Regression Intuition - Step 3 |

Unit 5 |
Multiple Linear Regression Intuition - Step 4 |

Unit 6 |
Multiple Linear Regression Intuition - Step 5 |

Unit 7 |
Multiple Linear Regression in Python - Step 1 |

Unit 8 |
Multiple Linear Regression in Python - Step 2 |

Unit 9 |
Multiple Linear Regression in Python - Step 3 |

Unit 10 |
Multiple Linear Regression in Python - Backward Elimination - Preparation |

Unit 11 |
Multiple Linear Regression in Python - Backward Elimination - HOMEWORK ! |

Unit 12 |
Multiple Linear Regression in Python - Backward Elimination - Homework Solution |

Unit 13 |
Multiple Linear Regression in R - Step 1 |

Unit 14 |
Multiple Linear Regression in R - Step 2 |

Unit 15 |
Multiple Linear Regression in R - Step 3 |

Unit 16 |
Multiple Linear Regression in R - Backward Elimination - HOMEWORK ! |

Unit 17 |
Multiple Linear Regression in R - Backward Elimination - Homework Solution |

Unit 18 |
Quiz 3 - Multiple Linear Regression |

Module 6 |
Section 6: Polynomial Regression |

Unit 1 |
Polynomial Regression Intuition |

Unit 2 |
Polynomial Regression in Python - Step 1 |

Unit 3 |
Polynomial Regression in Python - Step 2 |

Unit 4 |
Polynomial Regression in Python - Step 3 |

Unit 5 |
Polynomial Regression in Python - Step 4 |

Unit 6 |
Python Regression Template |

Unit 7 |
Polynomial Regression in R - Step 1 |

Unit 8 |
Polynomial Regression in R - Step 2 |

Unit 9 |
Polynomial Regression in R - Step 3 |

Unit 10 |
Polynomial Regression in R - Step 4 |

Unit 11 |
R Regression Template |

Module 7 |
Section 7: Support Vector Regression (SVR) |

Unit 1 |
SVR in Python |

Unit 2 |
SVR in R |

Module 8 |
Section 8: Decision Tree Regression |

Unit 1 |
Decision Tree Regression Intuition |

Unit 2 |
Random Forest Regression in Python |

Unit 3 |
Decision Tree Regression in R |

Module 9 |
Section 9: Random Forest Regression |

Unit 1 |
Random Forest Regression Intuition |

Unit 2 |
Random Forest Regression in Python |

Unit 3 |
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: ---------------------------- Part 3: Classification ---------------------------- |

There are no units in this module. |

Module 12 |
Section 12: Logistic Regression |

Unit 1 |
Logistic Regression Intuition |

Unit 2 |
Logistic Regression in Python - Step 1 |

Unit 3 |
Logistic Regression in Python - Step 2 |

Unit 4 |
Logistic Regression in Python - Step 3 |

Unit 5 |
Logistic Regression in Python - Step 4 |

Unit 6 |
Logistic Regression in Python - Step 5 |

Unit 7 |
Python Classification Template |

Unit 8 |
Logistic Regression in R - Step 1 |

Unit 9 |
Logistic Regression in R - Step 2 |

Unit 10 |
Logistic Regression in R - Step 3 |

Unit 11 |
Logistic Regression in R - Step 4 |

Unit 12 |
Logistic Regression in R - Step 5 |

Unit 13 |
R Classification Template |

Unit 14 |
Quiz 4 – Logistic Regression |

Module 13 |
Section 13: K-Nearest Neighbors (K-NN) |

Unit 1 |
K-Nearest Neighbor Intuition |

Unit 2 |
K-NN in Python |

Unit 3 |
K-NN in R |

Unit 4 |
Quiz 5 - K-Nearest Neighbor |

Module 14 |
Section 14: Support Vector Machine (SVM) |

Unit 1 |
SVM Intuition |

Unit 2 |
SVM in Python |

Unit 3 |
SVM in R |

Module 15 |
Section 15: Kernel SVM |

There are no units in this module. |

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 |
Naive Bayes in Python |

Unit 6 |
Naive Bayes in R |

Module 17 |
Section 17: Decision Tree Classification |

Unit 1 |
Decision Tree Classification Intuition |

Unit 2 |
Decision Tree Classification in Python |

Unit 3 |
Decision Tree Classification in R |

Module 18 |
Section 18: Random Forest Classification |

Unit 1 |
Random Forest Classification Intuition |

Unit 2 |
Random Forest Classification in Python |

Unit 3 |
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 ---------------------------- |

There are no units in this module. |

Module 21 |
Section 21: 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 |
K-Means Clustering in R |

Unit 6 |
Quiz 6 - K-Means 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 |
HC in Python - Step 1 |

Unit 5 |
HC in Python - Step 2 |

Unit 6 |
HC in Python - Step 3 |

Unit 7 |
HC in Python - Step 4 |

Unit 8 |
HC in Python - Step 5 |

Unit 9 |
HC in R - Step 1 |

Unit 10 |
HC in R - Step 2 |

Unit 11 |
HC in R - Step 3 |

Unit 12 |
HC in R - Step 4 |

Unit 13 |
HC in R - Step 5 |

Unit 14 |
Quiz 7 - Hierarchical Clustering |

Module 23 |
Section 23: ---------------------- Part 5: Association Rule Learning ---------------------- |

There are no units in this module. |

Module 24 |
Section 24: Apriori |

There are no units in this module. |

Module 25 |
Section 25: Eclat |

There are no units in this module. |

Module 26 |
Section 26: ------------------------ Part 6: Reinforcement Learning ------------------------ |

There are no units in this module. |

Module 27 |
Section 27: Upper Confidence Bound (UCB) |

There are no units in this module. |