What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.

Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the goal here is to understand the relationship between input variables and output variables. If I change the values of the inputs, how do the output values change? prediction: Here we are not as much interested in how the data changes, but just want to know the value of the output variable.

So, in general, you should be interested in Statistics, more specifically concerning prediction and inference. That's it, except that doesn't help you decide which books to purchase.

Here goes the list (it's a popular one)

# Books

**The Master Algorithm**

If you want to learn about machine learning algorithms in a relaxed and fun manner, good if to take up if the next books give you headaches. Certainly worth reading.

**An Introduction to Statistical Learning with Applications in R**

This book is the most approachable one in the list. It requires some understanding of mathematics to understand certain formulas, but the text is still written in a way that will make concepts clear before you dive into the math. Make sure you do the exercises with R. It's a good skill to pick-up and it will make the theory much more tangible.

This book and next one in the list are freely available online, but if you want you can still purchase paper versions on amazon. I linked you the free versions.

**The Elements Of Statistical Learning**

This one picks up where ISLR left off. it is more math heavy and explores new concepts. You will find some overlap with the first book which will help solidify the concepts you learned in the first book.

These first three books will already ease you quite into the field. However if you decide to become more serious about learning, the following books should definitely be on your reading list:

**Pattern Recognition and Machine Learning**

**Deep Learning**

**Reinforcement Learning**

The best advice I can give you with these books is to read them from cover to cover. Don't read too much at once, take breaks and try to explain what you read to yourself. It can often make sense on paper and then not so much when you say it aloud.
Don't look at the formulae as something to skip. Instead, look at them like lego blocks. Each symbol has a meaning that is defined in the index at the beginning of each book. Try to explain each symbol in the formula; Then explain how the symbols interact. Once you understand the formula, try to think what happens when certain symbols change values. You'll get a very firm grasp of the formula that way. The field of AI and ML has a **lot** of jargon it can become overwhelming. By really understanding how certain algorithms work you will stop being fooled by the fancy names and start to realize that there is a lot of repetition.

Enjoy !

I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any. – user36339 – 2019-04-04T09:05:33.913

@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses. – Sean Owen – 2019-04-04T09:36:24.220