Hands-On Machine Learning with Scikit-Learn and PyTorch

This book is an introduction to machine learning and deep learning. Aurélien Géron explains the ideas with clear examples, using Scikit-Learn, PyTorch and the Hugging Face ecosystem. It moves from basic regression techniques to advanced neural networks. Along the way it covers overfitting, hyperparameter tuning, clustering, anomaly detection, transformers, diffusion models, pretrained models including LLMs, and reinforcement learning. It is written for students, professionals and hobbyists who want to build intelligent systems.

Why You Should Read?

• Understand core machine learning ideas, such as overfitting and hyperparameter tuning, before you move on to harder topics.
• Complete a whole project with Scikit-Learn, working from data exploration all the way to model evaluation.
• Build advanced architectures like transformers and diffusion models with PyTorch, and try unsupervised methods such as clustering and anomaly detection.
• Learn to fine-tune pretrained models, including LLMs, and to train autonomous agents with reinforcement learning.