- Introduction
- Course Outline and Big Picture
- Where to get the Code
- How to Succeed in this Course
- Warmup
Online
₹ 2,999
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Artificial Intelligence: Reinforcement Learning in Python online certification is developed by Lazy Programmer, Artificial Intelligence and Machine Learning Engineer, Lazy Programmer Team and offered by Udemy Inc., a US-based online learning platform to help individuals boost their careers.
Artificial Intelligence: Reinforcement Learning in Python syllabus involves topics such as Markov decision processes, dynamic programming, Monte Carlo simulations, temporal difference (TD) learning (Q-Learning and SARSA), using Q-learning to create a stock trading bot, approximation method, using openAI gym, multi-brand bandit issues, explore-exploit dilemma, etc.
Artificial Intelligence: Reinforcement Learning in Python certification course is designed for the professionals working as data scientists, python programmers who have knowledge of the following topics: calculus (taking derivatives), matrix arithmetic, probability, python coding, NumPy coding, linear regression, and logistic regression as they are prerequisites for this course. Candidates who wish to enrol in the course must purchase a lifetime subscription and they can also avail the discounts available at the time of checkout.
The highlights
- Certificate of completion
- Self-paced course
- Online course
- English videos with multi-language subtitles
- 14.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos
- Multi-language subtitles
- 14.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Artificial Intelligence: Reinforcement Learning in Python online course, learners will get an understanding of reinforcement learning on a technological level, how reinforcement learning can benefit from gradient-based supervised machine learning approaches, learn about the link between psychology and reinforcement learning, implementation of 17 different reinforcement learning algorithms.
Who it is for
The syllabus
Welcome
Return of the multi armed bandit
- Section Introduction: The Explore-Exploit Dilemma
- Applications of the Explore-Exploit Dilemma
- Epsilon-Greedy Theory
- Calculating a Sample Mean (pt 1)
- Epsilon-Greedy Beginner's Exercise Prompt
- Designing Your Bandit Program
- Epsilon-Greedy in Code
- Comparing Different Epsilons
- Optimistic Initial Values Theory
- Optimistic Initial Values Beginner's Exercise Prompt
- Optimistic Initial Values Code
- UCB1 Theory
- UCB1 Beginner's Exercise Prompt
- UCB1 Code
- Bayesian Bandits / Thompson Sampling Theory (pt 1)
- Bayesian Bandits / Thompson Sampling Theory (pt 2)
- Thompson Sampling Beginner's Exercise Prompt
- Thompson Sampling Code
- Thompson Sampling With Gaussian Reward Theory
- Thompson Sampling With Gaussian Reward Code
- Why don't we just use a library?
- Nonstationary Bandits
- Bandit Summary, Real Data, and Online Learning
- (Optional) Alternative Bandit Designs
- Suggestion Box
High level overview of Reinforcement learning
- What is Reinforcement Learning?
- From Bandits to Full Reinforcement Learning
Markov decision process
- MDP Section Introduction
- Gridworld
- Choosing Rewards
- The Markov Property
- Markov Decision Processes (MDPs)
- Future Rewards
- Value Functions
- The Bellman Equation (pt 1)
- The Bellman Equation (pt 2)
- The Bellman Equation (pt 3)
- Bellman Examples
- Optimal Policy and Optimal Value Function (pt 1)
- Optimal Policy and Optimal Value Function (pt 2)
- MDP Summary
Dynamic Programming
- Dynamic Programming Section Introduction
- Iterative Policy Evaluation
- Designing Your RL Program
- Gridworld in Code
- Iterative Policy Evaluation in Code
- Windy Gridworld in Code
- Iterative Policy Evaluation for Windy Gridworld in Code
- Policy Improvement
- Policy Iteration
- Policy Iteration in Code
- Policy Iteration in Windy Gridworld
- Value Iteration
- Value Iteration in Code
- Dynamic Programming Summary
Monte Carlo
- Monte Carlo Intro
- Monte Carlo Policy Evaluation
- Monte Carlo Policy Evaluation in Code
- Monte Carlo Control
- Monte Carlo Control in Code
- Monte Carlo Control without Exploring Starts
- Monte Carlo Control without Exploring Starts in Code
- Monte Carlo Summary
Temporal Difference Learning
- Temporal Difference Introduction
- TD(0) Prediction
- TD(0) Prediction in Code
- SARSA
- SARSA in Code
- Q Learning
- Q Learning in Code
- TD Learning Section Summary
Approximation Methods
- Approximation Methods Section Introduction
- Linear Models for Reinforcement Learning
- Feature Engineering
- Approximation Methods for Prediction
- Approximation Methods for Prediction Code
- Approximation Methods for Control
- Approximation Methods for Control Code
- CartPole
- CartPole Code
- Approximation Methods Exercise
- Approximation Methods Section Summary
Interlude : Common Beginner Questions
- This Course vs. RL Book: What's the Difference?
Stock Trading Project with Reinforcement Learning
- Beginners, halt! Stop here if you skipped ahead
- Stock Trading Project Section Introduction
- Data and Environment
- How to Model Q for Q-Learning
- Design of the Program
- Code pt 1
- Code pt 2
- Code pt 3
- Code pt 4
- Stock Trading Project Discussion
Setting up your environment (FAQ by student request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra help with Python coding for beginners (FAQ by student Request)
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by student request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS