Artificial Intelligence and Machine Learning have become one of the hottest and most popular domains in the computer science and Future tech industry. So, Artificial Intelligence books are highly sough after today.

Every other company around the world is trying to implement Machine learning for better efficiency and transformation, or they are taking up machine learning projects for solving other company’s issues and developmental goals.

If you are that one person who is looking to explore this domain and make take up a new challenge, then we have listed out a few crucial artificial intelligence and Machine Learning books to start with your learning journey. 

Here is The List:

1. Introduction To Machine Learning With Python

It is an introductory book to machine learning, which is targeted for people who don’t have much knowledge or experience in Python.

It will teach you how to build your own machine learning solutions through various methods complemented by multiple sets of examples.

This can be called as the best textbook for beginner machine learning engineers or practitioners.

2. Artificial Intelligence: A Modern Approach

This book provides basic theoretical concepts of artificial intelligence. Beginners can consider this book as a complete reference. It is beneficial for students studying undergraduate or graduate-level courses in Artificial Intelligence.

The latest edition gives you in-depth information about the changes that have taken place in the domain of artificial intelligence from its last edition.

The tremendous practical implications of AI like actual speech recognition, machine translation, general robotics are all well explained in this book.

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Python Machine Learning is an excellent practical book that includes multiple examples of code. This book helps you in the natural understanding of the concepts and tools for developing and building intelligent/advanced systems.

You will learn several techniques and ways to start with basic linear regression and progressing towards deep neural networks.

With the help of practical exercises within each chapter to apply your learnings. It would be best if you had a basic understanding of programming.

4. Machine Learning With R

This comprehensive and conceptual book on the language ‘R’ will help you get insights from complex datasets and apply the correct algorithms for solving specific problems.

You will learn how to apply Machine Learning methods to deal with main tasks like forecasting, image categorization, prediction, and clustering.

Machine Learning with R will help you to acquire a brief understanding of a broad scope of subjects but can be possibly less suitable for those who want more in-depth insights in a particular field.

5. The Hundred Page Machine Learning Book

This book is a gem. It is a classic practical guide to get started and execute on Machine Learning within a few days without compulsorily knowing much about ML priorly. Linkedin superstar Andriy Burkov authors it

The first five chapters will get you started, and the next few sections provide you the confidence to pursue more advanced topics. ” rA wonderful book for engineers who want to learn ML in very less time without making efforts of learning through the professional degree program.

6. Deep Learning With Python

This book was written by a creator of Keras- François Chollet. Keras is one of the most well-known machine learning libraries in Python.

This book starts gently and then goes deep into the practical mode, gives multiple pieces of code you can use straight away, and has many tips in general that can help you in your quest for deep learning. A significant must-read book for people who have knowledge in deep learning.

7. Deep Learning (Adaptive Computation and Machine Learning series)

This deep learning book offers a mathematical background and relevant concepts in linear algebra, probability, and deep learning techniques.

The book describes many important deep learning techniques mostly used in the industry, which include deep feedforward networks, convolutional networks, optimization algorithms, sequence modeling, and practical methodology.

This book also offers details on research-related information like linear factor models, structured probabilistic models, autoencoders, partition function, etc.

8. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series)

This book is specially dedicated to practitioners who already have a good understanding of machine learning and trying to become an expert in this field.

If you are more mathematical oriented, it is the best book you will read with more machine learning methods which are most advanced in nature.

It is difficult to complete the book at once, but It has been proven to be the best and most comprehensive reference for Machine learners.  

9. Make Your Own Neural Network

This popular book has been authored by Tariq Rashid. It’s a gradual journey towards the mathematics of neural networks. Through Python programming language, you can create your own neural network with the help of this book.

In Part 1, different mathematical concepts of neural networks are discussed. Part 2 is thoroughly practical, which helps you to learn the Python language and helps you to create your own neural network recognizing human handwritten numbers and networks made by professionals.

Part 3 has extended the ideas further.

Machine Learning For Absolute Beginners (Machine Learning From Scratch)

This book is recommended for every absolute machine learning beginner. This book gives a practical and high-level priority to the practical as well as statistical concepts found in machine learning.

There is no coding experience required and all the concepts are explained in plain and simple English language. You will find all the core algorithms introductions are clearly explained and visual examples are added to make it easy and engaging for a home follow up.

This major new release consists of multiple features and topics which are missing in the first edition, including Cross- Validation, Data Scrubbing, and Ensemble Modeling. This book is an excellent restructured and revamped version of the First Edition and not a sequel to the first edition.


We hope all the required Artificial Intelligence Books are mentioned in this list. While there are tons of free e-learning Artificial intelligence and machine learning courses on the internet, books are still relevant even in these days. We would love to get more such alternative books from our readers in the comments section below.









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