Books like Machine learning by Ryszard S. Michalski

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.

A clear, concise guide to the fundamentals of machine learning, the branch of artificial intelligence that designs computer programs capable of learning from data and underpinning modern technologies like recommendation systems, face‑recognition, and autonomous vehicles.

A practical guide for professionals and researchers to select and apply the most suitable pattern recognition techniques across domains like speech, OCR, and image processing.

**Artificial intelligence**, or AI, refers to the capability of a computer or machine to mimic or pretend mortal intelligence and actions. This can include tasks similar as literacy, problem- working, decision- timber, language restatement, and more. There are different types of AI, including narrow or weak AI, which is designed for a specific task, and general or strong AI, which is designed to be suitable to perform any intellectual task that a human can. AI is frequently achieved through the use of machine literacy algorithms, which allow a machine to ameliorate its performance on a task over time by learning from data and once guests . Machine literacy can be supervised, where the machine is handed with labeled data and a set of rules to follow, or unsupervised, where the machine is given a set of data and must find patterns and connections within it on its own. AI has the implicit to revise numerous diligence and make tasks more effective and accurate. It's formerly being used in a variety of fields, similar as healthcare, finance, transportation, and client service. still, the development and use of AI also raises ethical and societal enterprises, including issues of bias, job relegation, and the eventuality for abuse.

A comprehensive textbook on machine learning that combines state‑of‑the‑art methods with hundreds of worked examples and clear explanations for both novices and experienced readers.

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.

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.