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

First, machine learning.

For the best introduction to the field of machine learning, please 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 algorithm, please check out this TutsPlus course "The Essence of Machine Learning".

The book "Programming Collective Wisdom" is a good resource to learn how to actually implement ML algorithm in Python. It requires you to go through many practical projects and cover all the necessary foundations.

You may also be interested in these good resources:

1, Udacity course on ML (ML Udacity Perer Norvig.

2. Another course on ML by Professor Tom Mitchell of Cameron University.

3. mathematicalmonk, a machine learning tutorial on 3.YouTube.

Second, deep learning.

The best introduction about deep learning is deep learning in Python. It does not enter difficult mathematics, nor does it have a long list of prerequisites. Instead, it describes a simple way to start DL and explains how to start building and learning everything quickly in practice. It explains the most advanced tools (Keras, TensorFlow) and shows you how to achieve the most advanced results in all the best DL applications through several practical projects.

There is also a great DL introductory course on Google, and Sephen Welch's great explanation of neural networks.

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

1, coursera course by Geoffrey Hinton, Neural Networks for Machine Learning. This course will take you to understand the classic problem of artificial neural network-the process of ——MNIST character recognition, and will explain everything in depth.

2. MIT deep learning.

3. Stanford University UFLDL tutorial (Stanford University UFLDL tutorial)

4.deeplearning.net tutorial

5. Michael Nelson's book Neural Network and Deep Learning.

6. Simon O. Haijin's book "Neural Networks and Learning Machines".

Third, artificial intelligence.

Artificial Intelligence: Modern Method (Liu Mengjie) is the best book about "old school" artificial intelligence. This book outlines 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 a series of excellent video lectures, which explain the basic knowledge through a very interesting practical project (training AI to play Pacman games). I recommend watching AIMA with the video, because it is based on this book and explains many similar concepts from different angles, making them easier to understand. Its explanation is in-depth and it is a very good resource for beginners.

How does the brain work

If you are interested in artificial intelligence, you may want to know how the human brain works. The following books will explain the best modern theories in an intuitive and interesting way.

1, On Intelligence by jeff hawkins (audio book)

2、G？ Del, escher, Bach

I suggest starting with these two books, which can explain the general theory of brain work to you very well.

Other resources:

Ray kurzweil's How to Create the Mind (audiobook).

Principles of Neuroscience is the best book I can find, which goes deep into NS. It talks about core science, neuroanatomy and so on. It's interesting, but it's also long-I'm still reading.

Fourth, mathematics.

Here are the very basic mathematical concepts you need to know when you start learning artificial intelligence:

calculus

1, Calculus Video of Khan Academy (Calculus Video of Khan Academy)

2. MIT Lecture on Multivariate Calculus (MIT Lecture on Multivariate Calculus)

linear algebra

1, Khan Academy Linear Algebra Video (Khan Academy Linear Algebra Video)

2. Gilbert Strong's MIT Linear Algebra Video

3. Matrix coding? (Coding Matrix)-CS Course of Thread Algebra in Brown University

Probability and Statistics

1, probability statistics video of Khan Academy

2.edx probability course (edx probability course)

Verb (abbreviation for verb) computer science

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

If you are just getting started, I suggest reading the book "Deep Python 3". Most of the knowledge needed for Python programming will be mentioned.

To understand the essence of computer programming more deeply, take a look at this classic MIT course (MIT is a course about lisp and the basics of computer science, and it is also one of the most influential books based on cs-structure and computer program explanation.

Other resources of intransitive verbs

Metaacademic? It is the "packaging manager" of your knowledge. You can use this great tool to learn all the prerequisites for learning different ML topics.

Cagle. –Machine learning platform