Books like Machine learning by Ethem Alpaydin

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow teaches practitioners to build intelligent systems with Python, covering core machine learning, deep learning, and TensorFlow 2 tools.

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

An Introduction to Statistical Learning offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

A concise overview of how computer programs learn from data and the implications of those learnings.

Explores the history and ethics of artificial intelligence, weighing the benefits and risks of creating intelligent machines and presenting differing viewpoints on the future of computer-driven learning.

The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these thingswork? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world-and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution-at least for now.