Best books on machine learning
You’re reading this, and you have questions. Where to start with Machine Learning? Which books to read? These are some common questions we hear from learners who want to learn more about Machine Learning but aren’t sure where they should start. We’ve been in your shoes before, as well as many other Machine Learning enthusiasts. The truth is that it’s not always easy to find resources for beginners or experts alike! So we thought it would be helpful to share the best resources and books with you so you don’t have to waste all your time trying to find everything by yourself. Note that these books are not just introductory books they can also be used as reference material by anyone associated with the field of machine learning and artificial intelligence. So you are going to find something of value in this list whether you are a professional who is working in the field of AI or a student who is currently enrolled in Machine Learning and AI courses in Bangalore or Chennai. So let’s get to know about these books:
“Machine Learning for Absolute Beginners: A Plain English Introduction” by Oliver Theobald
The book is aimed at beginners who want to learn machine learning. It’s a simple, short guide that walks you through all the essentials of ML, with clear explanations and lots of examples.
It starts with an introduction to machine learning, followed by more advanced topics such as neural networks and reinforcement learning. There are plenty of practical exercises that help you gain experience in both the theory and practice of applied machine learning.
The author does not use any codes or mathematics however, this book is an excellent choice for laying a foundation of the fundamentals before you move on to the advanced books.
2.”Hands-on ML” by Aurélien Géron
It provides a very practical guide to the use of machine learning algorithms in Python, with a lot of examples, exercises, and code snippets. The book also covers the basics of neural networks and deep learning, including how to build models from scratch, which makes it an ideal starting point for anyone who wants to learn more about these methods.
Having gone through this book, you will have a clear understanding of how it deals with the most popular libraries associated with machine learning, namely Scikit-Learn, Keras & TensorFlow2.
If you like those that combine very with practical exercises, this book is ideal for you. Some of the topics covered in this book include biological neurons, deepCV with the help of CNN, Supervised and unsupervised learning, and algorithm fundamentals.See more here from Download Free eBooks From Z Library
his is one of the best books that grow from beginner to advanced levels as there are no prerequisites for starting your journey on solving exercises mentioned in this book.
3.“Mathematics for machine learning” by Marc Peter Deisenroth
Mathematics is one of the fundamental pillars based on which a solid understanding of machine learning can be obtained. This is also one of the biggest barriers for people who don’t come from a mathematics background. While you could go back and study all the mathematics that you missed out on in high school, or you could simply opt for a book that covers all the fundamentals of mathematics needed for machine learning. . While many books talk about the mathematical concepts using machine learning, this one stands out because it also connects these mathematical concepts with the machine learning concepts. It can be used by beginners for learning and experts for reference. Some of the essential topics it covers include discrete mathematics, linear algebra, probability, and calculus. It is also a short book, so you can read it parallelly while continuing your study of other machine learning concepts or while being enrolled in a course. The fact that almost every reliable Machine Learning Course in Chennai and at other places recommends this book as supplementary material is proof of how good this book is.