Do you want to build a virtual self-driving car AI application using the most cutting-edge algorithm of Reinforcement Learning: Deep Q-Learning? Do you want to create an intelligence that can win the famous 90's game—DOOM—by using Deep Convolutional Q-Learning? Deep Q-Learning is the most robust and powerful technique in Artificial Intelligence for solving complex real-world problems. Artificial Intelligence is making our lives easy day by day and reducing human effort everywhere in social media, websites, online stores, and even business. With a less talk and more action approach, this course will lead you through various implementations of Reinforcement Learning techniques by building a virtual self-driving car application and an AI to beat the monsters in DOOM.

You may be wondering that why we create artificial intelligence in a game environment. That is because, once we have created our artificial intelligence in a gaming environment with the help of OpenAIGym, we can use those same principles to solve complex real-world problems just by changing and tweaking algorithm parameters. Get your hands on this course to learn the most fascinating technology in the field of Artificial Intelligence and leverage the power of Reinforcement Learning right away!

About the Author

Kaiser Hamid Rabbi is a Data Scientist who is super-passionate about Artificial Intelligence, Machine Learning, and Data Science. He has entirely devoted himself to learning more about Big Data Science technologies such as Python, Machine Learning, Deep Learning, Artificial Intelligence, Reinforcement Learning, Data Mining, Data Analysis, Recommender Systems and so on over the last 4 years. Kaiser also has a huge interest in Lygometry (things we know we do not know!) and always tries to understand domain knowledge based on his project experience as much as possible.