Python Machine Learning Book

This repository contains my personal notes and code implementations of the book Python Machine Learning by Sebastian Raschka. The book is a comprehensive guide to machine learning and deep learning with Python. It covers a wide range of topics including supervised and unsupervised learning, dimensionality reduction, ensemble learning, deep learning, and reinforcement learning. The book also provides practical examples and code implementations using popular Python libraries such as scikit-learn, TensorFlow, and Keras.

Table of Contents

Classification

1. Perceptron Study - Basic classification using the Perceptron algorithm.
2. Adaline Study - Adaline linear classifier with gradient descent optimization & stochastic gradient descent.
3. Maximum Margin with SVM Study - Using margins held on the backs of support vectors to reduce variance.
4. Kernal SVM Study - Becoming a higher dimensional being, to find linear relationships in a non-linear dataset.
5. Decision Tree Study - Divide and conquer, recursively splitting the dataset to find the best decision boundaries.
6. K-Nearest Neighbors - Gronk choose closest neighbor to choose classification 🗿

Preprocessing

1. Preprocessing - Fixing missing data and encoding and mapping labels & categorial data.
2. Feature scaling & feature selection - Data partitioning, feature scaling, feature selection, and Regularization with the wine Dataset.
3. Dimensionality Reduction - Unsupervised dimensionality reduction with PCA. Supervised dimensionality reduction with LDA. Kernal PCA for non-linear dimensionality reduction.