Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Here is my opinion, none of the books out there on Machine Learning cover all the topics needed to understand basics, underlying fundamentals and also how to program using myriad frameworks out there. The trick is to find the sources(books) that complement each other in filling this need. Here is one book that explains underlying fundamentals of ML in a very simple and intuitive way for starters. This is not meant for someone that has advanced mathematical background and intuitions, but I believe they too would benefit from the clarity this book adds. Also it explains some of the topics that are not generally elaborated well and rushed in most books, for example entropy, ID3, distance metrics etc. This a good complimentary book to everything I have in my bookshelf about ML. The price point of this books definitely stings though.

If you're aware of CRISP-DM, this book will give you a comprehensive walkthrough of the process. It takes you from data exploration through to evaluation with stunning depth in a surprisingly easy to follow narrative. Kelleher uses a case study for each chapter and discusses the strengths and weaknesses of the approaches (information-, similarity-, probability, and error-based learning). There are even additional chapters dedicated to case studies taking you through the CRISP-DM process. I find that I keep on opening the book to get to the theory and to evaluate the approaches. Highly recommended reading for novices in Machine Learning wanting to get a firm grip on the process.

I have already used machine algorithms in production with Spark and Python, but I wanted to have a better understanding of how the algorithms work and more importantly what the variations, strengths/weaknesses, and trade-offs are for each algorithm. This book was exactly what I've been looking for. The authors explain the algorithms fluidly without any reference to specific programming libraries or languages. They introduce the concepts very well before moving into the specifics of the logic and math behind the algorithms. Following a thorough explanation of how the algorithm works, the authors then describe variants and pitfalls based on their prior foundation. So, if you aren't a math major but would like to understand the concepts and details of how ML works along with practical knowledge of variants, parameter tuning, and trade-offs, then this book should be exactly what you need. Finally, the algorithms covered are the most commonly used in ML. AI isn't covered. Look at the Table of Contents to see which algorithms are explained.

A solid written, practical book. But could use more advanced knowledge to increase its usefulness. This machine learning book functions more of a basic reference.

An easy read. Exercises are helpful.

I am ML specialist and instructor. There are many different types of books in Machine Learning. That cover various aspects of the field. Some books are base on theoretic side: Learning from the Data. Some books provide a gentle way for programming for Machine Learning in different languages Some books combine theory and programming This book "Fundamentals of Machine Learning" a good written book for practitioner in machine learning. For people that want to know how machine learning experts work. That processes they use, and how them organize there work. In additional basic properties and ideas of general algorithms discussed. This book uses excellent plant English, many examples and real cases But if you need mathematical background or programming background I think you need use another book.

This is one of the best books on any subject I have read. Every aspect of this book -- approach, flow, content, theory, example, explanation -- is great. Reading this book was an excellent learning opportunity for me. The authors are dealing with a complicated topic of machine learning with such an ease and are practically explaining every concept/equation and its implementation. This will be a permanent addition to my library and will serve as excellent reference whenever I need to check relevant information.

Machine Learning is brilliantly explained in this outstanding book. You will learn the subject a lot better than in many other books in the market. The only downside of this book is the lack of examples with programming code, especially in Python. I strongly urge the authors to do so in a next edition. A lot in the area is learned by doing, by using good software development practices.

Good

One of the best books on Data Science with practical examples, data sets and solutions...

This book is used in my uni for machine learning, very useful and gives a new perspective to machine learning

Muy buen libro para iniciar en el tema de ML. Principalmente el área de prediccion Matemáticas simples

Of all the machine learning books I have read to date, I have found this book the most digestible. I like the way the book is structured: information-based learning, similarity-based learning, probability-based learning, error-based learning. Each chapter includes exercises that help reinforce learning but are enjoyable to tackle at the same time. The case studies provide further insights and reinforce the applied approach the book takes to understanding machine learning for predictive analytics.