10 Must-Read Books to Learn Machine Learning

Machine Learning is a subpart of Artificial intelligence and basically establishes its foundation. So, if you are intrigued by this new branch of Science then, understanding the Basics of Machine learning could help you in diving deeper into the subjects. If you are struggling to find the right material to start on with Machine Learning, well, then you are at the right place. We know how essential it is to refer and study using the right set of books. Thus, we have curetted a list of our top 10 Must read books to Learn Machine Learning.

The Hundred-page machine learning book

This is one of the best books to learn the basic concepts of Machine Learning in the most concise way. It is authored by Andriy Burkov. Published in the year 2019, it gives its reader an insight into what machine learning typically is. You could be a professional working in the domain of Artificial intelligence Training in Hong Kong or a newbie in it, this book will help you in every possible way. You can either go for the hardcover or simply buy the Kindle version. So, buy it today, Machine Learning in just 100 pages? What more could you ask for? Visit Here:  eblogz

Machine learning for absolute Beginners: A Plain English Guide

A marvelous collection of the idea of Machine learning through the POV of Oliver Theobold, this book takes you through each and every concept of Machine Learning in Layman’s language. So, if you aren’t a coder or a complete beginner to even the basics of Machine learning, then this could be your bible. It will guide you in the right direction and also help you understand AI or rather ML in the easiest way possible. Get your hands on this book today to get in touch with real ML.

Pattern Recognition and Machine Learning

In many situations, or problems, there are chances that exact answers are not feasible, in that case, this book authored by Christopher Bishop, gives you a guide to deal with these kinds of problems. This book covers topics such as Multivariate calculus and basic linear algebra. Released in 2007, this one was one of its kind to approach the subject of Machine learning during that time. If you are keen to study the development or advancement of ML through the years, well, then this one could be your starting point.

Grokking Deep Learning

Willing to build your algorithm from scratch? Grokking Deep Learning is the best option. This book is authored by Andrew W Trask and provides a Hands-on kind of approach to ML. it covers mostly the subparts of ML-like APIs but will act as a guide for creating algorithms. Although this is not one of those beginner guides, yet you can use it to be a complete newbie. You do not need any prerequisite aptitude for Calculus; even if you are well versed with General High school Mathematics, you could work with this one.

Introduction to Machine Learning with Python: A guide for data scientist

Specifically dedicated to the extensive use of Python in machine learning, this book provides a deep insight into Machine Learning through Python. Apart from all the concepts that are covered in the book, it also provides you with an understanding of where and how to use these concepts and algorithms. So, if you are looking to apply Python, then this book could come in handy for you. This book is authored by Sarah Guido and C. Muller jointly.

Mathematics for Machine Learning

This book is a blessing for all those who are beginners and who aren’t well versed with the basic mathematical tools that are used in ML. This book is a perfect amalgamation of nearly all the topics related to mathematics that you will need to develop the algorithm in ML. It includes topics like Linear algebra, Analytical geometry, Matrix decompositions, and much more. These form the basics of Machine learning. Apart from all the textual material that the book offers, it is abundantly filled with solves examples and questions for self-practice. With the practice tests papers, you can test your knowledge at every stage.

Hands-on Machine Learning with Scikit-Learn and tensor flow

This book takes you deep into the subject of Machine Learning. Predominantly into the topics like Neutral nets, Deep Reinforcement Learning, Eager executions, and much more. The only prerequisite is that you must be aware of the basic concepts of Python.  The best part of the book is that it contains all the updated code examples for some of the most used libraries and APIs involved. Visit Here: gopage7

Machine Learning in (Python and R) for Dummies

This series by the publisher is one of the most loved series. Basically made for the ‘Dummies’ or the beginners, this book contains all the Basics of Machine learning and all the tools that are used to write the algorithm, the programming languages used, and everything in great detail along with an extremely simple language. The book starts with an introduction on how to download R for Windows, Mac, and Linux. So, there are no prerequisites as such. You can completely rely on this one.

Machine Learning in Action

As the name suggests, this book, authored by Peter Harrington, is one of the best guides for people already working in the domain of Artificial Intelligence or is for the people who extensively use Machine Learning. You need a prior familiarity with Python to understand this. Be it Statistical data processing or data science; this book provides examples for each of them. Visit Here: wmt24

Machine Learning for Hackers: Case Studies and Algorithms to get you started

Written by Drew Conway and John Myles, this book is not exactly for hackers but the people who are willing to understand the case studies. This book gives its readers an insight into algorithms that drive Machine Learning. However, you need a strong programming background.

These were our top picks for the must-read books on Machine learning. Get them today to begin your journey towards ML!

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