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. |