Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]

This repository contains Jupyter notebooks implementing various machine learning algorithms covered in the Udemy course Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]. Each notebook provides hands-on implementation of key machine learning techniques using Python.

Course Sections & Algorithms

The course is divided into several sections, and the corresponding Jupyter notebooks for each algorithm are listed below.

I. Regression

1. Simple Linear Regression: simple_linear_regression.ipynb
2. Multiple Linear Regression: multiple_linear_regression.ipynb
3. Polynomial Regression: polynomial_regression.ipynb
4. Support Vector Regression (SVR): support_vector_regression.ipynb
5. Decision Tree Regression: decision_tree_regression.ipynb
6. Random Forest Regression: random_forest_regression.ipynb

II. Classification

1. Logistic Regression: logistic_regression.ipynb
2. K-Nearest Neighbors (KNN): k_nearest_neighbors.ipynb
3. Support Vector Machine (SVM): support_vector_machine.ipynb
4. Kernel SVM: kernel_svm.ipynb
5. Naive Bayes: naive_bayes.ipynb
6. Decision Tree Classification: decision_tree_classification.ipynb
7. Random Forest Classification: random_forest_classification.ipynb

III. Clustering

1. K-Means Clustering: k_means_clustering.ipynb
2. Hierarchical Clustering: hierarchical_clustering.ipynb

IV. Association Rule Learning

1. Apriori: apriori.ipynb
2. Eclat: eclat.ipynb

V. Reinforcement Learning

1. Upper Confidence Bound (UCB): upper_confidence_bound.ipynb
2. Thompson Sampling: thompson_sampling.ipynb

VI. Natural Language Processing

1. Natural Language Processing (NLP): natural_language_processing.ipynb

VII. Deep Learning

1. Artificial Neural Networks (ANN): artificial_neural_network.ipynb
2. Convolutional Neural Networks (CNN): convolutional_neural_network.ipynb

VIII. Dimensionality Reduction

1. Principal Component Analysis (PCA): principal_component_analysis.ipynb
2. Linear Discriminant Analysis (LDA): linear_discriminant_analysis.ipynb
3. Kernel PCA: kernel_pca.ipynb

IX. Model Selection & Boosting

1. K-Fold Cross Validation: k_fold_cross_validation.ipynb
2. Grid Search: grid_search.ipynb
3. XGBoost: xg_boost.ipynb

Resources

• Udemy Course: Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]