Top 5 Machine learning must-read book for every learner

VinLab
5 min readApr 5, 2023

Hello researcher or data scientist,

Reading is a conversation. All books talk. That’s why to have a “worthy conversation”, let’s discover what all these books say!

Note: Link free books is on the comment section!

What are all these books about?

“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurelien Geron

This book is a comprehensive guide to building machine learning models using some of the most popular Python libraries. The book is written in a clear and concise style, with a focus on practical examples and real-world applications.

In the first part, It covers the basics of machine learning, including supervised and unsupervised learning, feature engineering, and evaluation metrics. The author then introduces Scikit-Learn, a powerful Python library for machine learning, and shows how to use it to build various types of models, such as regression, classification, and clustering.

The second part of the book focuses on deep learning, and introduces Keras and TensorFlow, two popular Python libraries for building deep neural networks. The author explains the fundamentals of neural networks, including convolutional and recurrent neural networks, and shows how to use Keras and TensorFlow to build models for image classification, natural language processing, and more.

The numerous practical examples and code snippets that are included throughout. These examples are well-explained and help to reinforce the concepts covered in the text. In addition, the author provides numerous tips and best practices for building effective machine learning models, and covers common pitfalls and mistakes to avoid.

This book includes all the resources you could ask for in a machine learning textbook in addition to those found in the GitHub repository. If you want to get into machine learning but know very little or nothing about it, this is a great place to start.

“Machine Learning Yearning” by Andrew Ng

“Machine Learning Yearning” by Andrew Ng is a practical guide to machine learning for practitioners, written in a concise and accessible style. The book is divided into chapters that cover key topics in machine learning, such as model selection, hyperparameter tuning, and debugging.

One of the strengths of the book is the emphasis on practical advice and insights drawn from Andrew Ng’s years of experience as a machine learning practitioner. Each chapter is organized around a set of questions that practitioners commonly encounter when building machine learning models, and Ng provides clear and practical answers to these questions.

Also, it includes a number of case studies and examples that illustrate the concepts covered in the text. These examples are drawn from a range of domains, including computer vision, speech recognition, and natural language processing.

Another strength of the book is its focus on best practices and the importance of building scalable and maintainable machine learning systems. Ng emphasizes the need to focus on data quality, testing and debugging, and building modular and reusable code.

This book is best for practitioners looking to build effective machine learning models. Also, It is packed with insights and examples drawn from Andrew Ng’s extensive experience in the field.

Link free books in the comment of this post!

“The Hundred-Page Machine Learning Book” by Andriy Burkov

“The Hundred-Page Machine Learning Book” by Andriy Burkov is a concise and accessible introduction to machine learning for beginners. The book is organized into chapters that cover key topics in machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. It also where you can answer the question how to train a model to assign labels to objects; however, the goal is to predict labels that did not have any training data.

The book is easy for beginners to follow because of precise style. The author provides clear explanations of key concepts, and uses simple examples and illustrations to help readers understand the material.

Another strength of the book is its practical focus. The author provides a number of examples and case studies throughout the text, which help to illustrate the concepts covered in the book. In addition, the author provides practical advice on how to approach machine learning projects, including how to choose the right algorithm and how to avoid common pitfalls.

This book is an excellent resource for beginners looking to learn the basics of machine learning.

“Pattern Recognition and Machine Learning” by Christopher Bishop

The first part of the book covers the basics of pattern recognition, probability theory, and linear algebra, providing readers with a solid foundation for understanding the more advanced topics covered later in the book. The author then covers a variety of machine learning algorithms, including Bayesian methods, decision trees, and neural networks.

One of the focus of the book is the author’s focus on the underlying principles and mathematical foundations of machine learning, the practical advice and insights provided by the author. And numerous tips and best practices for building effective machine learning models, and covering common pitfalls and mistakes to avoid are included.

This book is most suitable for someone who is looking to deepen their understanding of machine learning and pattern recognition.

“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

The book starts with an introduction to the basics of machine learning, including supervised and unsupervised learning, and covers essential concepts such as cross-validation, overfitting, and regularization. The author then introduces readers to popular machine learning libraries, such as Scikit-Learn, Keras, and TensorFlow, providing numerous practical examples and code snippets to help readers get started quickly.

Also, author’s focus on practical advice and best practices for building effective machine learning models. He covers a wide range of topics, including data preprocessing, feature engineering, hyperparameter tuning, and model selection, and provides numerous tips and tricks for building effective models.

Another strength of the book is the author’s approach to deep learning. The author covers the basics of deep learning and provides practical examples of building deep learning models using Keras and TensorFlow, including convolutional neural networks, recurrent neural networks, and autoencoders.

Suitable for anyone looking to learn practical machine learning techniques and frameworks. The book provides a solid foundation for building effective machine

learning models.

Thanks for reading!

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