This book focuses on the high-level fundamentals of machine learning as well as the mathematical and statistical underpinnings of designing machine learning models.

Machine learning algorithms are often divided into three general categories (though other classification schemes are also used): supervised learning, unsupervised learning, and reinforcement learning.

Students in my Stanford courses on machine learning have already made several useful suggestions, as have my colleague, Pat Langley, and my teaching assistants, Ron Kohavi, Karl P eger, Robert Allen, …

This course provides a broad introduction to machine learning paradigms including supervised, unsupervised, deep learning, and reinforcement learning as a foun- dation for further study or …

This book is by no means claiming that you have to learn data science to keep your job, and that everyone needs to understand machine learning programming to have a job in the future.

This book introduces the fundamentals of machine learning for absolute beginners, emphasizing the importance of classical statistics and programming skills in developing machine learning models.

This book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introduc‐tory book requiring no previous knowledge …

Looking for Machine Learning Books? Here we present 20 books and materials that you can download for free and print in your home.

It is written with the hope to provide the reader with a deeper 13 understanding of the algorithms made available to her in multiple machine learn-ing packages and software, and that she will be able to …

This tutorial will give an introduction to machine learning and its implementation in Artificial Intelligence. This tutorial has been prepared for professionals aspiring to learn the complete picture of machine …