In a step by step manner, enhance your skills and master advanced concepts in reinforcement learning with practical examples.

Video Description

Reinforcement learning (RL) is becoming popular and is used as a tool for constructing autonomous systems that improve themselves with experience. This video course will provide the viewer with advanced practical examples in R and Python. You will learn about Q-Learning, Deep Q-Learning, Double Deep Q-Learning, Reinforcement Learning in TensorFlow, and Reinforcement Learning in Keras. The practical example is provided throughout the course such as TensorFlow for RL with practical examples, Taxi Routes, with an in-depth exploration of Keras— a Practical example to help a car reach the hilltop.

You will learn how to code convolutional neural networks for deep reinforcement learning, and how to use modern tools such as Google’s TensorFlow and Keras. You will also be exposed to case studies related to reinforcement learning. By the end of the video course, you will able to take your machine learning skills to the next level with reinforcement learning techniques and you will have mastered programming the environment for Reinforcement Learning.

All the code and supporting files for this course are available on GitHub at

Style and Approach

The course is a step-by-step guide to understanding Reinforcement learning. Throughout the courseis experienced there are practical, real-world examples that will help you get acquainted with the various concepts of reinforcement learning. This course provides practical reinforcement examples in R and Python.

What You Will Learn

  • Explore Deep Q-Learning, neural networks, TensorFlow, and Keras
  • How to start coding with Q-Learning
  • Master Deep Q-Learning, Neural Networks, and Convolutional Neural Networks
  • Get fancy with TensorFlow for Reinforcement Learning
  • Explore Keras in depth for Reinforcement Learning
  • Build Deep Q-Learning in TensorFlow
  • Build Double Q-Learning in Keras
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Lauren Washington

Lauren Washington is currently the Lead Data Scientist and Machine Learning Developer for smartQED (, an AI driven startup. Lauren worked as a Data Scientist for Topix, Payments Risk Strategist for Google (Google Wallet/Android Pay), Statistical Analyst for Nielsen, and Big Data Intern for the National Opinion Research Center through the University of Chicago.Lauren is also passionate about teaching Machine Learning. She’s currently giving back to the data science community as a Thinkful Data Science Bootcamp Mentor ( and a Packt Publishing technical video reviewer.She also earned a Data Science certificate from General Assembly San Francisco (2016), a MA in the Quantitative Methods in the Social Sciences (Applied Statistical Methods) from Columbia University (2012), and a BA in Economics from Spelman College (2010).Lauren is a leader in AI, in Silicon Valley, with a passion for knowledge sharing, I can’t think of a better author for this course.



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