Machine learning and AI have taken the entire world by storm. The possibilities that they offer are limitless. Machine learning is a set of techniques and tools that help computers learn and adapt on their own. It has a wide array of applications in various fields ranging from space exploration to digital marketing. Being proficient in machine learning algorithms is also a great way to kickstart your career. This article gives you a list of the top machine learning books that you must read in 2022.
Top 5 Machine Learning Books
Machine Learning for Absolute Beginners: A Plain English Introduction
Written by Oliver Theobald and published by the Scatterplot Press, Machine Learning for Absolute Beginners is a book that acts as the ideal entry point for anyone wishing to explore the world of ML and AI. Requiring little to no coding or mathematical background, all the concepts in the book have been explained very clearly. This book simplifies a lot of the complex theories associated with ML such as Data Scrubbing, Regression Analysis, Clustering and more. This book also provides a lot of visuals which follow examples to make learning easier for beginners.
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville is widely regarded as one of the best machine learning books out there. It is a very beginner-friendly book that introduces you to deep learning and also comprehensively covers topics of machine learning. The book does an amazing job of explaining topics such as Linear Algebra, Probability and Information Theory, Optimization Algorithms, Convolutional Networks, Monte Carlo methods and Partition Function.
The Hundred-Page Machine Learning Book
Can machine learning really be covered in just 100 pages? The Hundred-Page Machine Learning Book by Andriy Burkov is an effort to do the same. Written to be understood easily, this book will help you build and appreciate complex AI systems, clear an ML-based interview, and start your career with an edge over the competition. This book is also endorsed by industry leaders such as Director of Research at Google, Peter Norvig and Sujeet Varakhedi, Head of Engineering at eBay.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron
Written by Aurelien Geron, this book is easily one of the most popular machine learning books on the market right now. Prior knowledge of python is needed to properly comprehend this book as it explains some of the most-used ML libraries Scikit-Learn, Keras, and TensorFlow 2, for building intelligent systems. The various other topics explained in this book are Support Vector Machines, Random Forests, Neural Nets, Deep Reinforcement Learning and more.
Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran
Written in 2007, Programming Collective Intelligence: Building Smart Web 2.0 Applications is a trailblazer of a book that teaches more about implementing ML than just being an introduction to the field of study. The book makes use of Python as the vehicle for delivering the knowledge to its readers. Some of the topics covered include Bayesian filtering, Collaborative filtering techniques, Evolving intelligence for problem-solving, Search engine algorithms and more.
The above mention machine learning books are a must-read for anyone interested in venturing into the world of ML and AI. Mastering ML is added advantage when looking for a job in the world of data science. Praxis understands the importance of machine learning and has curated a state of the art PGP in Data Science to teach young aspirants the same. This program, with its exemplary placement records and astounding tie-ups, can indeed help you elevate your skills and start your career on a high note.
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