Practical AI with Python and Reinforcement Learning

Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs!

4.44 (1226 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Reinforcement Learning with Python

Creating Artificial Neural Networks with TensorFlow

Using TensorFlow to create Convolution Neural Networks for Images

Using OpenAI to work with built-in game environments

Using OpenAI to create your own environments for any problem

Create Artificially Intelligent Agents

Tabular Q-Learning

State–action–reward–state–action (SARSA)

Deep Q-Learning (DQN)

DQN using Convolutional Neural Networks

Cross Entropy Method for Reinforcement Learning

Double DQN

Dueling DQN

Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

Practical AI with Python and Reinforcement Learning offers valuable insights into using reinforcement learning techniques in artificial intelligence programs. However, it is best suited for those with a strong understanding of data science and some experience in Machine Learning. The course could benefit from updating the Reinforcement Learning section to ensure its content remains relevant and accurate. Overall, this course provides a solid foundation for applying AI strategies; however, users seeking real-world examples beyond gaming applications might be left wanting more.

What We Liked

Excellent for those with a strong background in data science, particularly Python proficiency and some Machine Learning experience.
In-depth explanations of algorithms theory and history provide a solid foundation for understanding general AI application strategies.
Detailed discussions on coding help eliminate confusion for students.

Potential Drawbacks

The course focuses solely on applying AI to games, with limited exploration of AI use outside of gaming applications.
Outdated content in the Reinforcement Learning section and Gym library has left some users unable to follow along.
Code errors have been reported, requiring students to research solutions or fix issues independently.