About this Course
In this course, you will learn the paradigm of reinforcement learning in python which is more different from supervised and unsupervised learning than they are from each other. Reinforcement learning has recently become popular, as we all know that Artificial Intelligences’ 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. Reinforcement learning opens up a whole new world.
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 they have so far to a true general artificial intelligence.
The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.
“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“
–Max Tegmark, President of the Future of Life Institute
About Reinforcement learning in Python
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment to maximize the notion of cumulative reward. Reinforcement learning is one of the basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Though reinforcement learning differs from supervised learning in not needing labeled input/output pairs to be presented and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between the exploration of uncharted territory and the exploitation of current knowledge.
What you will learn from the Reinforcement Learning in Python Course
- Apply gradient-based supervised machine learning methods to reinforcement learning.
- Understand the relationship between reinforcement learning and psychology.
- Understand reinforcement learning on a technical level.
- Implement 17 different reinforcement learning algorithms.
In this AI’s reinforcement learning in python course, you will be learning:
1. Reinforcement Learning in Python: Return of the Multi-Armed Bandit
- Section Introduction & Application of the Explore-Exploit Dilemma.
- Epsilon-Greedy Theory, Calculating a Sample Mean (pt 1) and Epsilon-Greedy Beginner’s Exercise Prompt.
- Designing Your Bandit Program, Epsilon-Greedy in Code, and Comparing Different Epsilons.
- Optimistic Initial Values Theory, Beginner’s Exercise Prompt & Code.
- Optimistic Initial Values UCB1 Theory, Beginner’s Exercise Prompt & Code.
- Bayesian Bandits / Thompson Sampling Theory Part 1 and Part 2.
- Thompson Sampling Beginner’s Exercise Prompt and code and Thompson Sampling With Gaussian Reward Theory and code.
- Non-stationary Bandits, Bandit Summary, Real Data, and Online Learning with Alternative Bandit Designs.
2. Reinforcement Learning in Python: High Level of Overview Reinforcement Learning
- On Unusual or Unexpected Strategies of RL.
- From Bandits to Full Reinforcement Learning.
3. Reinforcement Learning in Python: Markov Decision Processes
- MDP Section Introduction, grid world.
- The Markov Property and Markov Decision Processes (MDPs)
- The Bellman Equation– Part 1, Part 2, and Part 3 with Bellman Examples.
- Optimal Policy and Optimal Value Function Part 1 and Part 2.
- MDP Summary.
4. Reinforcement Learning in Python: Dynamic programming
- Intro to Dynamic Programming and Iterative Policy Evaluation.
- Designing Your RL Program, Grid world in Code, and Iterative Policy Evaluation in Code.
- Windy grid world in Code.
- Iterative Policy Evaluation for Windy grid world in Code, Policy Improvement, Iteration & Iteration in Code.
- Value Iteration and Iteration in Code.
- Dynamic Programming Summary.
5. Reinforcement Learning in Python: Monte Carlo
- Monte Carlo Intro, Policy Evaluation, and Evaluation in Code
- Policy Evaluation in Windy grid world.
- Monte Carlo Control and Control in Code.
- Monte Carlo Control without Exploring Starts and Exploring Starts in Code
6. Reinforcement Learning in Python: Temporal Difference Learning
- Temporal Difference Intro.
- TD(0) Prediction and Prediction in Code.
- SARSA and SARSA in Code.
7. Reinforcement Learning in Python: Approximation Method
- Approximation Intro.
- Linear Models for Reinforcement Learning and Features.
- Monte Carlo Prediction with Approximation and Approximation in Code.
- TD(0) Semi-Gradient Prediction.
- Semi-Gradient SARSA and SARSA in Code.
8. Stock Trending Project with Reinforcement Learning in Python
- Stock Trading Project Section Introduction.
- Data and Environment.
- How to Model Q for Q-Learning.
- Design of the Program.
- Code Part1,Part2,Psrt3 and Part4.
- Stock Trading Project Discussion.
- Object-oriented programming
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations
- Linear regression
- Gradient descent
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- Lazy Programmer Team Inc.
- Online Course
- 1-4 Weeks
- Paid Course (Paid certificate)
- Artificial intelligence Data Science with 'Python' Machine learning