Machine Learning involves studying computer algorithms and statistical models for a specific task using patterns and inference instead of explicit instructions. And there is no doubt that Machine Learning is an insanely popular career choice today. Here are top selling machine learning books.
Hundred-page machine learning
It is written by Andry Burkov is the perfect book to discover machine learning without getting into nitty-gritty details. It’s not easy to summarize the core elements of a complex and broad discipline like machine learning. After reading the book, you’ll be ready to discuss all kinds of topics related to machine learning, including supervised and unsupervised learning, the most popular machine learning algorithms, and what it takes to build and fine-tune a model. Math, intuition, and illustrations, all in just a hundred pages.
Fundamentals of Machine Learning
It is written by John D. Kelleher, Brian Mac Namee. This textbook is especially well-suited for professionals with an analytical background. The second edition of Machine Learning for Predictive Data Analytics provides a comprehensive introduction to machine learning approaches, covering both theory and practice. Detailed examples illustrating the applications of machine learning models in the real world support the technical and mathematical explanations. Examples range from price prediction and risk assessment to document classification and predicting customer behavior. The second edition also incorporates new chapters on deep learning and machine learning techniques beyond predictive analytics, including unsupervised learning and reinforcement learning.
Data Mining
Practical Machine Learning Tools and Techniques offers a highly accessible introduction to machine learning concepts. And along with mathematical theory and practical advice on applying these techniques in real-world situations. The book’s fourth edition includes new chapters to reflect the latest developments in the field. Also including probabilistic methods and deep learning. It is also worth mentioning that the book comes with the authors’ own software, WEKA, a comprehensive collection of machine learning algorithms for data mining tasks in an easy-to-use interactive interface.
Machine learning
It is written by Kevin P. Murphy, a research scientist at Google, is a journey through the mathematics behind the most common machine learning algorithms. It offers an informal yet detailed explanation of key topics, such as probability, optimization, and linear algebra. The book contains complete pseudo-code for the most important algorithms, images, and examples covering machine learning domain applications in biology, computer vision, robotics, and so on.
Reinforcement Learning
One of the fields in machine learning that has seen more progress recently is reinforcement learning. That is, a method of machine learning where an agent learns to perform certain actions in an environment which lead it to maximum reward. If you’re interested in this field, you should read this book. Despite the fact that the word “Introduction” appears in the title, Reinforcement Learning provides a thorough account of the key ideas and algorithms of reinforcement learning. The second edition of the book, published in 2018. Hence, includes new topics that have appeared in the last few years.
Machine learning books is one of the most useful skills within data science. There is a growing number of machine learning books that can help you break into the field or become an expert.
