Artificial Intelligence: Reinforcement Learning in Python
Duration: 14h 42m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 3.82 GB
Genre: eLearning | Language: English
Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications
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
- Understand important foundations for OpenAI ChatGPT, GPT-4
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
Ever wondered how AI technologies likeOpenAIChatGPTandGPT-4really work? In this course, you will learn the foundations of these groundbreaking applications.
When people talk aboutartificial intelligence, they usually don’t mean supervised and unsupervisedmachine learning.
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 learninghas recently become popular for doing all of that and more.
Much likedeep 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 sawGoogle’sAlphaGobeat 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 times.
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)
UNIQUE FEATURES
Every line of code explained in detail – email me any time if you disagree
No wasted time "typing" on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math – get important details about algorithms that other courses leave out
Who this course is for:
- Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning
- Both students and professionals
More Info
5.7 (3,782 Votes)
