Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases Paperback – 31 July 2024

The best way to learn is by using real world examples. If you want to learn stock forecasting, build ML models using regression, NN and LSTM. By doing this you will learn a lot about how to build ML models, and how to forecast stock prices. And that is exactly what Hayden does in this book. He teaches us, step by step, using real world examples.This book is 500 pages, and he only spent 34 pages discussing the theory. The rest of the book covers various interesting cases like Online Advertising, Stock Price Forecasting, News Clustering, Face Recognition, Image Categorisation and Image Search Engine. Hayden cleverly uses those real world cases to teach us various ML algorithms: CNN, RNN, LSTM, GPT, K-Mean, Regression, Decision Tree, Random Forest, Gradient Boosting, SVM and Naive Bayes. And he does it step by step, very clearly. And of course LLM too i.e. Transformer (BERT), NLP basics, and Open AI Gym for Reinforcement Learning.Hayden is a veteran in ML, he has written ML books before. And ML is his day job too, at Google no less. It is very rare that someone with that much experience in the field took their time to write their experience down in a book. This book is a treasure for me, as it enables me to understand how to build all the above interesting cases. It is very comprehensive, and we learn using real world examples. I recommend this book to data scientists at every level. Yes you'll get a lot from it if you are a beginner, and you'll get a lot from it if you already work in ML for years.

I just finished reading Python Machine Learning By Example (Fourth Edition) by Yuxi (Hayden) Liu, and I can't recommend it highly enough! This book is an outstanding resource for anyone looking to deepen their understanding of machine learning with Python.What makes this book stand out is its perfect balance between theory and hands-on application. It starts with the fundamentals and gradually builds up to advanced topics like deep learning, transformers, and multimodal models. Each chapter is structured around real-world use cases—whether it's building recommendation systems, predicting stock prices, or working with NLP models like BERT and GPT.Some of the highlights for me:✔️ Clear and concise explanations of ML algorithms✔️ Practical coding examples that reinforce learning✔️ Coverage of cutting-edge techniques like CLIP and reinforcement learning✔️ Best practices that make the transition from theory to real-world projects seamlessWhether you're a beginner looking to break into ML or an experienced data scientist seeking to refine your skills, this book provides immense value. It’s engaging, thorough, and packed with insights from an industry expert.