Artificial Intelligence: Reinforcement Learning in Python (updated 5/2021)

What you'll learn

Apply gradient-based supervised machine learning methods to reinforcement learning

Understand reinforcement learning on a technical level

Understand the relationship between reinforcement learning and psychology

Implement 17 different reinforcement learning algorithms

Requirements

Calculus (derivatives)

Probability / Markov Models

Numpy, Matplotlib

Beneficial to have experience with at least a few supervised machine learning methods

Gradient descent

Good object-oriented programming skills

Description

These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.

Reinforcement learning has recently become popular for doing all of that and more.

Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible.

In 2016 we saw Google's AlphaGo beat the world Champion in Go.

We saw AIs playing video games like Doom and Super Mario.

Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.

Learning about supervised and unsupervised machine learning is no small feat. To date I have over TWENTY FIVE (25!) courses just on those topics alone.

And yet reinforcement learning opens up a whole new world. As you'll learn in this course, the reinforcement learning paradigm is very from both supervised and unsupervised learning.

It's led to new and amazing insights both in behavioral psychology and neuroscience. As you'll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It's the closest thing we have so far to a true artificial general intelligence. What's covered in this course

The multi-armed bandit problem and the explore-exploit dilemma

Ways to calculate means and moving averages and their relationship to stochastic gradient descent

Markov Decision Processes (MDPs)

Dynamic Programming

Monte Carlo

Temporal Difference (TD) Learning (Q-Learning and SARSA)

Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)

How to use OpenAI Gym, with zero code changes

Project: Apply Q-Learning to build a stock trading bot

If you're ready to take on a brand new challenge, and learn about AI techniques that you've never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

See you in class!

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 s...

Suggested Prerequisites:

Calculus

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Linear regression

Gradient descent

WHAT ORDER SHOULD I TAKE YOUR COURSES IN:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning

Both students and professionals



DOWNLOAD
uploadgig


rapidgator


nitro