How do beginners learn artificial intelligence from scratch? After reading you will understand

This article is a list of the best learning resources for beginners who want to enter the field of artificial intelligence but do not know where to start.

 First, machine learning

For the best introduction to the field of machine learning, watch Coursera's Andrew Ng machine learning course. It explains the basic concepts and gives you a good understanding of the most important algorithms.

• For a brief overview of ML algorithms, check out this TutsPlus course "Machine Learning Distilled".
• The book "Programming Collective Intelligence" is a great resource to learn about practical implementations of ML algorithms in Python. It requires you to go through many hands-on projects covering all the necessary foundations.

You may also be interested in these great resources:

1. Udacity Course on ML by Perer Norvig
2. Tom Mitchell's Another course on ML at Cameron University
3. Machine learning tutorial mathematicalmonk on YouTube

Second, deep learning

The best introduction to deep learning, the best I have encountered is Deep Learning With Python. It doesn't go deep into difficult math, nor does it have a prerequisite for an extremely long list, but instead describes a simple way to start DL, explaining how to quickly start building and learning everything in practice. It explains state-of-the-art tools (Keras, TensorFlow) and takes you through several practical projects to explain how to achieve state-of-the-art results in all the best DL applications.

There is also a great introductory DL course on Google, as well as a great explanation of neural networks by Sephen Welch.

Then, for a deeper understanding, here are some interesting resources:

1. Geoffrey Hinton's coursera course "Neural Networks for Machine Learning". This course will take you through the classic problem of ANN-the process of MNIST character recognition, and will explain everything in depth.
2. MIT Deep Learning.
3. UFLDL tutorial by Stanford
4. deeplearning.net tutorial 
5. Neural Networks and Deep Learning by Michael Nielsen
6. Neural Networks and Learning Machines by Simon O. Haykin

Third, artificial intelligence

"Artificial Intelligence: A Modern Approach (AIMA)" is the best book on "old school" AI. This book gives an overview of the field of artificial intelligence and explains all the basic concepts you need to know.

The Artificial Intelligence course from the University of California, Berkeley is an excellent series of video lectures that explain the basics through a very interesting practical project (training AI to play Pacman games). I recommend reading AIMA together with the video, because it is based on this book and explains a lot of similar concepts from different angles to make them easier to understand. Its explanation is relatively deep and is a very good resource for beginners.

How the brain works

If you are interested in artificial intelligence, you may be wondering how the human brain works. The following books will explain the best modern theory in an intuitive and interesting way.

1. Jeff Hawkins' On Intelligence (audiobook)
2. Gödel, Escher, Bach

I recommend getting started with these two books, they can explain to you the general theory of brain work.

Other resources:

How to Create a Mind by Ray Kurzweil (Audiobook).

Principles of Neural Science is the best book I can find to dive into NS. It talks about core science, neuroanatomy, etc. Very interesting, but also long — I'm still reading it.

Fourth, mathematics

Here are the very basic mathematical concepts you need to know to start learning AI:

Calculus

1. Khan Academy Calculus videos
2. MIT lectures on Multivariable Calculus

Linear algebra

1. Khan Academy Linear Algebra videos
2. MIT linear algebra videos by Gilbert Strang (Gilbert Strang's MIT linear algebra video)
3. Coding the Matrix-Brown University Thread Algebra CS Course

Probability and statistics

1. Khan Academy Probability and Statistics Video
2. edx probability course

Five, computer science

To master AI, you need to be familiar with computer science and programming.

If you are just getting started, I recommend reading the book Dive Into Python 3, where most of the knowledge you need in Python programming is mentioned.

For a deeper understanding of the nature of computer programming-see this classic MIT course. This is a course on the basics of Lisp and Computer Science, based on one of the most influential books in CS-Structure and Computer Program Interpretation.
Six, other resources
Metacademy-is the "package manager" of your knowledge. You can use this great tool to understand all the prerequisites you need to learn different ML topics.

• kaggle-Machine learning platform