PracticalAIwithPythonandReinforcementLearning

An application-focused course on creating intelligent agents that learn through trial and error within dynamic environments. Explores Deep Q-Learning, SARSA, and the Cross-Entropy method to solve complex decision-making problems.

TensorFlow

Keras

OpenAI Gym (Gymnasium)

NumPy

Matplotlib

Practical AI with Python and Reinforcement Learning

Learning in Progress

Currently mastering the advanced concepts of this course.

Instructor

Jose Portilla, Pierian Training

Duration

26.5 hours

Platform

Udemy

PracticalAIwithPythonandReinforcementLearning

Learning in Progress

Course Curriculum

Course Overview0/2In Progress

Course Set-Up and Installation Procedures0/2In Progress

Numpy Basics Overview0/2In Progress

Matplotlib and Visualization Overview0/2In Progress

Machine Learning, Deep Learning, and Reinforcement Learning0/1In Progress

Pandas and Scikit-Learn Crash Course0/2In Progress

Artificial Neural Network and TensorFlow Basics0/3In Progress

Convolutional Neural Networks with TensorFlow0/3In Progress

Reinforcement Learning - Core Concepts0/3In Progress

OpenAI Gym Overview0/2In Progress

Classical Q-Learning0/3In Progress

Deep Q-Learning0/4In Progress

Deep Q-Learning on Images0/3In Progress

Creating Custom OpenAI Gym Environments0/2In Progress

Additional RL Methods0/2In Progress