Artificial Intelligence: Reinforcement Learning in Python

Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications

4.60 (10600 reviews)

Udemy

platform

English

language

Data Science

What you will 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

Course Gallery

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Artificial Intelligence: Reinforcement Learning in Python – Screenshot 4 — Screenshot 4 – Artificial Intelligence: Reinforcement Learning in Python

Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This advanced course on Reinforcement Learning in Python is a comprehensive guide, providing in-depth knowledge of RL algorithms and their applications. While it assumes strong background knowledge, the author's teaching style is appreciated by learners. The course could benefit from clearer code explanations and more repetition to cater to those with differing learning styles.

What We Liked

Comprehensive guide to Reinforcement Learning (RL) with practical applications in Stock Trading and Online Advertising
In-depth coverage of 17 different RL algorithms and their implementation
Thorough understanding of the relationship between RL and psychology
Author's teaching style is appreciated, encouraging individual coding style and emphasizing algorithm consistency
Exposes students to the math behind RL with formulas highlighted in the code

Potential Drawbacks

Course is advanced, requires strong background knowledge including probability, optimization, calculus, and Python
Lacks extensive listing of problem types where RL can be applied
Can be challenging for those without sufficient pre-requisite knowledge due to the quick pacing and complex explanations
May benefit from more repetition and inclusion of general markers for easy reference when skipping over material
Code explanations could be clearer, students may struggle to understand pre-written code