This is a list of the best resources for beginners who want to get into artificial intelligence but don’t know where to start.

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 the TutsPlus course “Machine Learning Distilled.”
  • The book “Programming Collective Intelligence” is a great resource for learning about the actual implementation of ML algorithms in Python. It requires you to go through a number of practical projects, covering all the necessary foundations.

Here are some great resources you might also be interested in:

  1. Perer Norvig’s Udacity Course on ML
  2. Another Course on ML by Tom Mitchell at Cameron University
  3. Peter: MathematicalMonk, the machine learning course on YouTube

Deep learning

The best introduction to Deep Learning I’ve come across is Deep Learning With Python. It doesn’t delve into difficult math, nor does it have a super-long list of prerequisites, but rather describes a simple way to start the DL, explaining how to quickly start building and learn everything practically. It explains state of the art tools (Keras, TensorFlow) and takes you through several practical projects, explaining how to achieve state of the art results in all the best DL applications.

There is also a Great Introductory DL course on Google and Sephen Welch’s Great Explanation of Neural Networks.

After that, here are some interesting resources for a more in-depth look:

  1. Geoffrey Hinton’s Coursera course Neural Networks for Machine Learning. This course will take you through ANN’s classic problem, the MNIST character recognition process, 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. Simon O. Haykin: Neural Networks and Learning Machines

Artificial intelligence

Artificial Intelligence: A Modern Approach (AIMA) is one of the best books on “old school” AI. This book provides a general 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 a series of excellent video lectures that explain the basics through a very interesting hands-on project (training AI to play Pacman). I recommend reading AIMA together with the video, as it is based on the book and explains many similar concepts from different perspectives, making them easier to understand. It’s relatively deep and a great resource for beginners.

How the Brain Works

If you’re interested in artificial intelligence, you might be curious to know how the human brain works. Here are a few books that explain the best modern theories in intuitive and interesting ways.

  1. On Intelligence by Jeff Hawkins (Audio Book)
  2. Gödel, Escher, Bach

I recommend getting started with these two books, which do a good job of explaining to you the general theory of how the brain works.

Other resources:

  1. How to Create a Mind by Ray Kurzweil (audiobook).
  2. Principles of Neural Science is the best book I could find, deep in NS. It talks about core science, neuroanatomy and so on. Very interesting, but also very long – I’m still reading it.

Four, mathematics

Here are some very basic mathematical concepts you need to know to get started with AI:

calculus

  1. Khan Academy Calculus Videos
  2. MIT Lectures on Multivariable Calculus

Linear algebra

  1. Khan Academy Linear Algebra Videos Khan Academy Linear Algebra Videos
  2. MIT Linear Algebra Videos by Gilbert Strang MIT Linear Algebra Videos by Gilbert Strang
  3. Coding the Matrix — Thread Algebra CS at Brown University

Probability and statistics

  1. Khan Academy video of Probability and Statistics
  2. Edx Probability Course

5. Computer Science

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

If you’re just getting started, I recommend reading Dive Into Python 3, which covers most of what you’ll need to know in Python programming.

For a deeper understanding of the nature of computer programming, see this classic MIT course. This course is about lisp and the basics of computer science, based on one of the most influential books in the CS – Structure and Computer Program interpretation.

Other resources

  1. Metacademy – Is the “package manager” for your knowledge. You can use this great tool to understand all the prerequisites you need to learn about different ML topics.
  2. Kaggle — Machine learning platform