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

It will covers from the most basic concepts

Each chapter takes you through a new machine learning project, so you can see how the concepts you’re learning are actually applied. For example, it covers everything from building simple classifiers to creating recommendation systems, which is pretty awesome if you’re looking to build something practical.What I also loved was the hands-on approach the book took. Each chapter includes exercises that really make you think about how to apply the concepts to new problems. It’s one thing to read about machine learning, but it’s another thing entirely to actually practice and solve problems with it.The writing is clear and simple without being dumbed down. I’m the kind of person who gets easily frustrated when a book goes over my head or assumes I know more than I actually do. Luckily, that didn’t happen here. Every bit of code is explained in a way that makes sense, and I wasn’t left feeling lost. Yuxi does a great job explaining the logic behind each piece of code, which is so important when you’re just starting out. For once, I felt like I actually understood why the code worked, not just how to copy-paste it.Plus, this book has a whole chapter on "Machine Learning Best Practices" with 21 Best Practices.This book is an incredible resource. It’s clear, practical, and packed with real-world examples that make learning machine learning feel less intimidating. Whether you’re a total beginner or someone who’s already played around with Python, this book is definitely worth checking out. I can’t recommend it enough.

Starting my journey in machine learning was both exciting and overwhelming. I struggled to bridge the gap between theory and practical application in real-world projects. That’s why Yuxi Hayden Liu’s "Python Machine Learning by Example" has been a game-changer for me. This book offers a structured approach, making it easier to transition from learning to execution.Liu covers essential topics like overfitting, underfitting, and cross-validation right from the start, ensuring that you grasp the fundamentals. What truly sets this book apart is the hands-on projects that accompany each concept. From building a movie recommendation engine using Naive Bayes to predicting stock prices and exploring deep learning through artificial neural networks, Liu walks you through each step—from data preparation to model evaluation.The book is rich with best practices, such as feature engineering, algorithm selection, and monitoring model performance. By the end, you'll not only have a solid understanding of basic and advanced topics, including CNNs, transformer models, and reinforcement learning, but you’ll also feel confident applying them in real-world scenarios.Yuxi Hayden Liu’s industry experience shines through, making this book an invaluable guide for anyone feeling lost in their machine learning journey. Highly recommended for both students and professionals looking to elevate their skills. Happy reading!