Machine Learning Books

Where to buy: This book is distributed on the ''read first, buy later" principle, which means first you can read this book online, and when you think this is helpful, then you can buy it on the Amazon marketplace site.

Amazon Link: https://www.amazon.com/Hundred-Page-Machine-Learning-Book-ebook/dp/B07MGCNKXB

3. Building Machine Learning Powered Applications: Going from Idea to Product, written by Emmanuel Ameisen

Why should you read this book?

Emmanuel Ameisen spent 13 months writing this 250-page book, which includes practical instructions for shipping machine learning. This book can be very helpful if you want to learn how to design, build, and deploy machine learning-powered applications because it ends with a hands-on exercise that takes your ideas from machine learning models to production. Due to the clear, step-by-step explanation of machine learning applications, this book is appreciated by all data scientists, software engineers, product managers, and experts. There are three different copies of this book. You can learn how to plan a machine learning model and measure success in the first section. You can learn how to construct a machine learning model in the second section. In the third part, you can learn techniques to work on the model to satisfy your unique vision. In addition, you can develop deployment and monitoring strategies in the fourth and final part.

Where to buy: This book is highly recommended by data scientists, software engineers, and product managers. You can purchase this book on Amazon or O'Reilly Shop.

Amazon Link: https://www.amazon.com/Building-Machine-Learning-Powered-Applications/dp/149204511X/

O'Reilly Shop: https://www.oreilly.com/library/view/building-machine-learning/9781492045106/

4. Grokking Deep Learning, written by Andrew W. Trask

Why should you read this book?

Andrew W.Trask wrote the book Understanding Deep Learning. Mr. Andrew has demonstrated how to construct a brand-new deep learning neural network in this book. Utilizing just Python and maths supporting libraries, NumPy, you will prepare your own brain organizations to see and figure out pictures, make an interpretation of text into different dialects and even compose like William Shakespeare. At the point when you're finished, you'll be completely ready to continue on toward dominating profound learning structures.

Where to get it: Using NumPy's low-level building blocks to learn machine learning and neural networks are covered in detail in this book. This book is available for purchase through Manning Publications or Amazon.

Amazon Link:https://www.amazon.com/Grokking-Deep-Learning-Andrew-Trask/dp/1617293709

Manning Publications: https://www.manning.com/books/grokking-deep-learning

5. Deep Learning with Python written by Francois Chollet

Why should you read this book?

This book comprises of centre ideas of profound picking up utilizing the python language and Keras library. Francois Chollet, who is well known for the production of Keras and Google Man-made consciousness scientist, composed this book with natural clarifications and pragmatic models. This book assists you with investigating centre ideas and their pragmatic applications in PC vision, NLP, and learning models. After fulfillment of this book, you will get to know all involved abilities as well as a hypothetical comprehension of profound picking up utilizing python language and libraries.

Where to get it: Before purchasing this book, readers should be familiar with basic Python. In addition, this book can be very helpful even if you are just starting out with Keras, TensorFlow, and the Machine Learning field. This book is available for purchase on the websites of O'Reilly Media, Manning Publications, and Amazon Marketplace. The following are links:

Amazon Link: https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/

Manning Publications: https://www.manning.com/books/deep-learning-with-python

O'Reilly: https://www.oreilly.com/library/view/deep-learning-with/9781617294433/

6. Deep Learning written by Ian Goodfellow, Yoshua Bengio, Aaron Courville:

Why should you read this book?

This book is considered the Bible of Deep Learning, written by three experts Ian Goodfellow, Yoshua Bengio, Aaron Courville. Although this book is full of technical mathematics principles and authors, have explained each concept in a perfect manner, but if you want to start your journey in a deep learning journey, then this is not recommended. Because, to understand all the concepts, first you need to build your Algebraic foundation, then only you can consider this book.

Linear Algebra, Probability Theory, Information Theory, Numerical Computing, and Machine Learning are all covered in depth in this book. The authors of this book made it very clear how to use deep learning techniques, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Further, other than profound learning advances, you can improve information on different applications, for example, regular language handling, discourse acknowledgment, PC vision, online suggestion frameworks, bioinformatics, and videogames. The partition function, approximate inference, deep generative models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, and other theoretical topics are all covered in this book.

Where to buy: This book can be very helpful for students as well as experts or researchers who are planning to do some different in this industry. You can purchase this book on Amazon.

Amazon Link: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/

7. Reinforcement Learning: An Introduction (2nd Edition) written by Richard S. Sutton, Andrew G. Barto

Why should you read this book?

This book is accessible in different classes, for example, AI, Support Learning, Profound Learning, Profound Support Learning, and Computerized reasoning.

Mr. Richard S. Sutton and Andrew G. Barto wrote this book. In the event that Profound Learning book (referenced above) is thought of as the Authoritative manual for Profound Learning, then this book is additionally viewed as the Guidebook for Support Learning. If you truly have any desire to begin a profession in the Support Learning field, then, at that point, this book can be exceptionally useful for you.

The author has provided a thorough explanation of their straightforward concepts regarding AI algorithms in this book. Like the principal release, the subsequent version is additionally around centre learning calculations like UCB, Expected Sarsa, and Double Learning. Further, this book is conveyed in different parts, which incorporates themes, for example, Fourier basis, policy gradient methods, reinforcement learning's relationships to psychology and neuroscience, AlphaGo, AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy.

Where to buy: You can purchase this book on the Amazon marketplace and also read free online on the below-given link.

Amazon link: https://www.amazon.com/dp/0262039249/

Read here free PDF: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

8. "Deep Reinforcement Learning Hands-On (2nd Edition)" written by Maxim Lapan:

Why should you read this book?

Mr. Maxim Lapan wrote this book, which helps you understand the practical methods of reinforcement learning by balancing theory and practice, including coding. According to various surveys, if you truly need to acquire involved insight with hypothetical information on support learning, then, at that point, this book is best appropriate. This book is likewise accessible in different classes, for example, Machine Learning, Reinforcement Learning, Deep Learning, Deep Reinforcement Learning, and Artificial Intelligence.

Where to buy: You can purchase this book on Amazon or the Packt website.

Amazon link: https://www.amazon.com/Deep-Reinforcement-Learning-Hands-optimization/dp/1838826998

Packt Link: https://www.packtpub.com/product/deep-reinforcement-learning-hands-on/9781788834247

9. "Learning From Data" written by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin.

Why should you read this book?

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin are the three authors of this book. To upgrade your insight about the center ideas of AI, then this is the best book to follow.

This book contains the total presentation of AI and is unreservedly accessible to get to on the web. Machine learning is used in a lot of different fields, like engineering, science, finance, and business, among others. This innovation assists you with empowering a computational framework and work on the exhibition through old records. As a result, this book is meant to serve as a primer on machine learning and covers fundamental topics that both topics and contained researchers should to be familiar with.

Where to Buy:

This book is available online for free access and designed in e-chapters, and regularly updated with current trends in Machine Learning. You can purchase this book on Amazon also.

Amazon Link: https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069

10. "The Book of Why" written by Judea Pearl, Dana Mackenzie:

Why should you read this book?

This book is combinedly composed by Judea Pearl, Dana Mackenzie, and it is the most questionable book accessible on this rundown. In this book, the writer presents the causality system that beats bend fitting AI or Deep Learning models and furthermore shares their contemplations to accomplish AI.

The idea that "correlation is not causation" serves as the foundation for this book.

You will learn how to manage and think about simple things, as well as how to respond to difficult questions, after reading this book. Further, this book shows us the embodiment of human idea and the way to AI.

Where to Buy:

If you want to enhance your thinking capability, then this book is probably the best available book over the internet. You can purchase this book on Amazon.

Amazon Link: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X