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Chapters

This textbook is organized into 12 chapters covering 200 concepts in machine learning.

Chapter Overview

  1. Introduction to Machine Learning Fundamentals - Introduces core ML terminology, paradigms, data concepts, features, labels, models, and algorithms—establishing the foundation for all subsequent learning.

  2. K-Nearest Neighbors Algorithm - Covers the intuitive KNN algorithm including distance metrics, k-selection, decision boundaries, and applications to classification and regression.

  3. Decision Trees and Tree-Based Learning - Explores decision tree structure, splitting criteria, pruning, overfitting/underfitting concepts, and feature space partitioning.

  4. Logistic Regression and Classification - Introduces logistic regression with sigmoid functions, binary and multiclass classification, and probabilistic interpretations.

  5. Regularization Techniques - Covers regularization methods to prevent overfitting including L1/L2 regularization, Ridge and Lasso regression.

  6. Support Vector Machines - Comprehensive coverage of SVMs including hyperplanes, margins, kernel trick, and various kernel types.

  7. K-Means Clustering and Unsupervised Learning - Explores k-means clustering with centroids, initialization strategies, and cluster evaluation methods.

  8. Data Preprocessing and Feature Engineering - Covers essential preprocessing including normalization, standardization, encoding, and feature engineering techniques.

  9. Neural Networks Fundamentals - Comprehensive introduction to neural networks including architecture, activation functions, backpropagation, and gradient descent.

  10. Convolutional Neural Networks for Computer Vision - Covers CNNs including convolution operations, pooling layers, and famous architectures like ResNet and VGG.

  11. Transfer Learning and Pre-Trained Models - Explores transfer learning concepts including fine-tuning, feature extraction, and domain adaptation.

  12. Model Evaluation, Optimization, and Advanced Topics - Comprehensive coverage of evaluation metrics, cross-validation, optimization techniques, and hyperparameter tuning.

How to Use This Textbook

Progress through the chapters sequentially, as each chapter builds on concepts from previous chapters. The dependency structure ensures that prerequisite knowledge is always covered before more advanced topics. Each chapter includes:

  • Conceptual explanations with mathematical foundations
  • Practical implementation examples using Python
  • References to Jupyter notebooks in the Code folder
  • Exercises and real-world applications

Note: Each chapter includes a complete list of concepts covered. Make sure to master the fundamentals in early chapters before advancing to neural networks and deep learning topics.