Artificial Intelligence (AI): Reinforcement Learning in Python.

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Product is rated as #25 in category Data Science
Learner rating9.2
  • Course platform: Udemy
  • Level: Intermediate
  • Full lifetime access
  • Price: Paid course
  • Class length: Approx. 13hrs

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AI’s Reinforcement Learning Python course

What is Artificial Intelligence?

In simple words, Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

Artificial Intelligence: Reinforcement Learning in Python.

“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 in order to maximize the notion of cumulative reward. Reinforcement learning is one of the basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement Learning in Python.

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 exploration of uncharted territory and the exploitation of current knowledge.

About this Course on Reinforcement Learning Python

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine 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 learning has recently become popular for doing all of that and more. 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. Reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.

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.

What you will learn from the AI’s Reinforcement Learning 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.

Syllabus

In this AI’s reinforcement learning python course, you will be learning:

1. 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. High Level of Overview Reinforcement Learning

  • On Unusual or Unexpected Strategies of RL.
  • From Bandits to Full Reinforcement Learning.

3. 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. 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. 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. Temporal Difference Learning

  • Temporal Difference Intro.
  • TD(0) Prediction and Prediction in Code.
  • SARSA and SARSA in Code.

7. 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 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.

Prerequisites:

  • Calculus
  • Probability
  • 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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FAQ

9.2Expert Score
Learner Experience
The score is based on the user experience, rated by the learners
Learner rating
9.2
PROS
  • Excellent veteran instructor’s explanation and teaching process.
  • All math is thoroughly explained and ambiguities clarified.
  • It will absolutely clear your theory and math concept.
CONS
  • Should give focus on more use of animations and visual learning.
  • Need improvement in balance between theory of RL and practical’s.

Specification: Artificial Intelligence (AI): Reinforcement Learning in Python.

Course Platform Udemy
Level Intermediate
Class length <1 week
Program details Course
Enrollment Paid Course (paid certificate)
Course Subjects Artificial intelligence, Data Science with 'Python', Machine learning
Offered by Organization Lazy Programmer Team Inc.

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Artificial Intelligence (AI): Reinforcement Learning in Python.
Artificial Intelligence (AI): Reinforcement Learning in Python.