Machine Learning A-Z

This repository features Python implementations of a wide range of machine learning models that I explored during the Machine Learning A-Z course. The models cover Regression, Classification, Clustering, Reinforcement Learning, Association Rule Learning, Natural Language Processing (NLP), as well as Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN)

Repository Structure

Machine Learning A-Z/
├── Model Selection/
│   ├── Classification
│   └── Regression
├── Part 1 - Data Preprocessing
├── Part 2 - Regression/
│   ├── Section 4 - Simple Linear Regression
│   ├── Section 5 - Multiple Linear Regression
│   ├── Section 6 - Polynomial Regression
│   ├── Section 7 - Support Vector Regression (SVR)
│   ├── Section 8 - Decision Tree Regression
│   └── Section 9 - Random Forest Regression  
├── Part 3 - Classification/
│   ├── Section 14 - Logistic Regression
│   ├── Section 15 - K-Nearest Neighbors (K-NN)
│   ├── Section 16 - Support Vector Machine (SVM)
│   ├── Section 17 - Kernel SVM
│   ├── Section 18 - Naive Byes
│   ├── Section 19 - Decision Tree Classification
│   └── Section 20 - Random Forest Classification
├── Part 4 - Clustering/
│   ├── Section 24 - K-Means Clustering
│   └── Section 25 - Heirarchical Clustering
├── Part 5 - Association Rule Learning/
│   ├── Section 28 - Apriori
│   └── Section 29 - Eclat
├── Part 6 - Reinforcement Learning/
│   ├── Section 32 - Upper Confidence Bound (UCB)
│   └── Section 33 - Thompson Sampling
├── Part 7 - Natural Language Processing  
├── Part 8 - Deep Learning/
│   ├── Section 39 - Artificial Neural Network (ANN)
│   └── Section 40 - Convolutional Neural Network (CNN) 
├── Part 9 - Dimensionality Reduction/
│   ├── Section 43 - Principal Component Analysis (PCA)
│   ├── Section 44 - Linear Discriminant Analysis (LDA)
│   └── Section 45 - Kernel PCA
└── Part 10 - Model Selection and Boosting/
    ├── Section 48 - Model Selection
    ├── Section 49 - XGBoost
    └── Section 50 - CatBoost

Course Certificate