Python Machine Learning By Example, Third Edition

Machine Learning That Starts With Working Code 💻

Python Machine Learning By Example, Third Edition is built around a simple idea: the fastest way to understand machine learning is to build working systems. Yuxi (Hayden) Liu guides readers through practical Python projects that turn algorithms into functioning code, using the tools modern practitioners actually rely on.

What the Third Edition Covers

This edition has been updated for the current Python machine learning ecosystem. The book moves from core concepts into implementation with scikit-learn, TensorFlow 2, and PyTorch. Readers work through data preparation, model training, evaluation, and improvement while building projects that show how the pieces fit together.

• Foundations of machine learning, including generalization, overfitting, cross-validation, and regularization
• Data preprocessing and feature engineering with NumPy, SciPy, and Pandas
• Classification and recommendation systems using Naïve Bayes
• Face recognition with Support Vector Machines and PCA
• Online ad click-through prediction with decision trees, random forests, and logistic regression
• Modern deep learning workflows with TensorFlow 2 and PyTorch

From Naïve Bayes to Neural Networks 🧠

The chapters follow a deliberate progression. Early examples, such as a movie recommendation engine, introduce classification and evaluation. Later projects, including face recognition and ad click-through prediction, add more sophisticated algorithms and larger datasets. The result is a path that builds both intuition and technical fluency.

Why the Example-First Approach Works ⚙️

Machine learning concepts can stay abstract until you see them operate on real data. This book keeps the focus on implementation: how to prepare features, choose a model, measure performance, and iterate. Code is not treated as an afterthought; it is the main teaching tool. That makes the material useful for readers who want to move from theory to something they can run and modify.

Who This Book Is For

Python developers, data scientists, analysts, and students with some programming experience will find the book approachable. If you are comfortable with Python basics and want a project-driven introduction to machine learning, the examples offer a practical way to build skill. The third edition also suits readers who used earlier editions and want to work with TensorFlow 2, PyTorch, and updated scikit-learn practices.

What You’ll Need 💡

The book includes setup guidance for Python environments and the main libraries used throughout: NumPy, SciPy, Pandas, scikit-learn, TensorFlow, and PyTorch. A working Python installation and curiosity about how models behave are the main prerequisites.

Build Skills That Transfer to Real Projects 📊

By working through the examples, readers develop a reusable workflow: explore data, engineer features, train models, evaluate results, and improve. Those skills apply across recommendation, classification, prediction, and deep learning tasks. For anyone building a foundation in Python machine learning, this third edition offers a clear, hands-on route.