Hands-On Machine Learning

Reading Progress

Started: January 16, 2026

Part 1: Fundamentals (Scikit-Learn)

Chapter 1: The Machine Learning Landscape

Chapter 2: End-to-End ML Project

Chapter 3: Classification

Chapter 4: Training Models

Chapter 5: Support Vector Machines

Chapter 6: Decision Trees

Chapter 7: Ensemble Learning and Random Forests

Chapter 8: Dimensionality Reduction

Chapter 9: Unsupervised Learning Techniques

Part 2: Neural Networks (Keras & TensorFlow)

Chapter 10: Introduction to Artificial Neural Networks

Chapter 11: Training Deep Neural Networks

Chapter 12: Custom Models and Training with TensorFlow

Chapter 13: Loading and Preprocessing Data

Chapter 14: Deep Computer Vision Using CNNs

Chapter 15: Processing Sequences Using RNNs and CNNs

Chapter 16: Natural Language Processing with RNNs and Attention

Chapter 17: Autoencoders, GANs, and Diffusion Models

Chapter 18: Reinforcement Learning

Chapter 19: Training and Deploying TensorFlow Models at Scale

Overview Part 1 covers the foundations of machine learning using Scikit-Learn. It focuses on classical ML algorithms, data preprocessing, model evaluation, and building production-ready pipelines. Chapters Chapter 1: The Machine Learning Landscape What is Machine Learning? Types of ML Systems Main Challenges of ML Testing and Validating Chapter 2: End-to-End Machine Learning Project Working with Real Data Data Exploration and Visualization Preparing Data for ML Algorithms Select and Train a Model Fine-Tune Your Model Chapter 3: Classification MNIST Dataset Training a Binary Classifier Performance Measures Multiclass Classification Error Analysis Multilabel and Multioutput Classification Chapter 4: Training Models Linear Regression Gradient Descent Polynomial Regression Regularized Linear Models (Ridge, Lasso, Elastic Net) Logistic Regression Chapter 5: Support Vector Machines Linear SVM Classification Nonlinear SVM Classification SVM Regression Under the Hood Chapter 6: Decision Trees Training and Visualizing Decision Trees Making Predictions Estimating Class Probabilities CART Training Algorithm Regularization Hyperparameters Regression Instability Chapter 7: Ensemble Learning and Random Forests Voting Classifiers Bagging and Pasting Random Forests Boosting (AdaBoost, Gradient Boosting) Stacking Chapter 8: Dimensionality Reduction The Curse of Dimensionality Main Approaches for Dimensionality Reduction PCA Kernel PCA LLE, MDS, Isomap, t-SNE Chapter 9: Unsupervised Learning Techniques Clustering (K-Means, DBSCAN, etc.) Gaussian Mixtures Anomaly Detection Novelty Detection Learning Goals By the end of Part 1, I should be able to: ...

Hands-On Machine Learning

About This Book

Why I’m Reading This

Book Structure

Part 1: The Fundamentals of Machine Learning

Part 2: Neural Networks and Deep Learning

Reading Progress

Part 1: Fundamentals (Scikit-Learn)

Part 2: Neural Networks (Keras & TensorFlow)

Practice Repositories

Part 1 Practice

Part 2 Practice

Official Resources

Notes & Insights

Part 1: The Fundamentals of Machine Learning

About This Book#

Why I’m Reading This#

Book Structure#

Part 1: The Fundamentals of Machine Learning#

Part 2: Neural Networks and Deep Learning#

Reading Progress#

Part 1: Fundamentals (Scikit-Learn)#

Part 2: Neural Networks (Keras & TensorFlow)#

Practice Repositories#

Part 1 Practice#

Part 2 Practice#

Official Resources#

Notes & Insights#

About This Book

Why I’m Reading This

Book Structure

Part 1: The Fundamentals of Machine Learning

Part 2: Neural Networks and Deep Learning

Reading Progress

Part 1: Fundamentals (Scikit-Learn)

Part 2: Neural Networks (Keras & TensorFlow)

Practice Repositories

Part 1 Practice

Part 2 Practice

Official Resources

Notes & Insights