Machine Learning: Beginner Reinforcement Learning in Python
What you'll learn
- Machine Learning
- Artificial Intelligence
- Neural Networks
- Reinforcement Learning
- Deep Q Learning
- OpenAI Gym
- Keras
- Tensorflow
- Bellman Equation
Course content
- Preview05:20
- 3 questionsPractise Activity: Machine Learning Quiz
- Preview07:00
- 2 questionsPractise Activity: Reinforcement Learning Quiz
- Preview02:37
- 1 questionPractise Activity: Nchain Quiz
- 00:41Reviewing your Practise Activity
Requirements
- Basic knowledge of Python
Description
This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification.
We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward. Using this example I will teach you Deep Q Learning - a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari.
Who this course is for:
- Anyone interested in machine learning
Instructor
![Milo Spencer-Harper](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.7 Instructor Rating
- 320 Reviews
- 989 Students
- 2 Courses
After studying at Oxford University, I was struck by how the best professors made very complex ideas easy to understand. That is my mission. I believe in bringing the breakthroughs occurring in artificial intelligence from inaccessible academic papers to anyone who wants to learn.
Over 600,000 students have read my blog post "How to build a neural network in 9 lines of Python code". I'm excited to bring courses on machine learning and artificial intelligence to Udemy.