Are you interested in developing intelligent bots for gaming using the power of Deep Learning and Machine Learning? Then you're in the right place! Artificial Intelligence for Simple Games is an excellent course that teaches you how to use Deep Reinforcement Learning and Artificial Intelligence tools on examples of simple games. SuperDataScience has designed this course as an enjoyable tool to build your AI knowledge and intuition gradually with exciting case studies. The course includes both basic and advanced concepts that give you a solid foundation to build AI systems within a gaming environment and beyond.
Course Description
Artificial Intelligence for Simple Games is a comprehensive course that teaches you how to use Deep Reinforcement Learning and Artificial Intelligence tools on examples of simple games. The course provides a flexible and fun environment that you can use to explore your AI knowledge of fundamental Deep and Machine Learning algorithms. The course covers algorithms such as Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks. Whether you're a novice or seasoned professional, you'll find the curriculum to be a solid foundation for your AI-building journey.
Course Objective
The primary objective of the Artificial Intelligence for Simple Games course is to teach students how to build intelligent bots using Deep Reinforcement Learning and Artificial Intelligence tools. Students will learn fundamental ML and DL algorithms, such as Genetic Algorithms, Q-Learning, Deep Q-Learning, Artificial Neural Networks, and Convolutional Neural Networks.
Students will have the opportunity to explore the OpenAI Gym development environment and build AI for many other simple games. The course offers practical real-world case studies that challenge students to experiment with different game scenarios, apply their learning, and develop intuition.
Curriculum
The Artificial Intelligence for Simple Games curriculum is structured into four sections. Students can learn each section at their own pace, and they do not need prior experience in coding, AI or Machine Learning. The course sections are:
Section #1 — Dive into Genetic Algorithms
In the Artificial Intelligence for Simple Games course, students will start with an introduction to Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge of this section is to build a spaceship that travels across all planets in the shortest possible time!
Section #2 — Learn the foundations of the Model-free Reinforcement Learning Algorithm, Q-Learning
Students will learn Model-free Reinforcement Learning Algorithms such as Q-Learning in this section. Students will develop intuition and visualization skills and try their hand at building a custom maze and designing an AI that can find its way out.
Section #3 — Go deep with Deep Q-Learning
The course's third section explores how to go deep with Deep Q-Learning by utilizing Neural Networks using the OpenAI Gym development environment that teaches how to build AIs for many other simple games. Students will learn how Deep Q-Learning functions in this section and how to use it for gaming.
Section #4 — Build your very own version of the classic game, Snake
The course ends by teaching students how to recreate their own version of the classic game, Snake. In this section, students will use Convolutional Neural Networks to build an AI that mimics the same action players experience while playing Snake.
Course Rating Aggregate
Artificial Intelligence for Simple Games has a course rating aggregate of 4.44718, based on 208 course reviews. It is a testament to the quality of the course materials and instructions given by SuperDataScience.
Code Flexibility
The Artificial Intelligence for Simple Games course has great code flexibility. As part of the course, students will experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry. This flexibility provides students with the freedom and versatility to expand their knowledge of Artificial Intelligence into practical real-world scenarios beyond gaming.
Why Take This Course?
If you're interested in developing intelligent bots for gaming or want to learn how to use Deep Reinforcement Learning and Artificial Intelligence tools, the Artificial Intelligence for Simple Games course is perfect for you.
This course provides a flexible and fun environment where you can explore fundamental Machine Learning and Deep Learning algorithms. Students will dive into practical, yet challenging case studies and experiment with different game scenarios to apply what they have learned. The case studies are accessible to both novices and seasoned professionals seeking to perfect their AI-building journey.
Lastly, with the course's code flexibility, students can easily apply their learning to business problems outside of the gaming industry. Whether you're a novice or experienced professional, the Artificial Intelligence for Simple Games course is an interesting and engaging tool that can provide students with invaluable AI knowledge to expand their AI-building skills.
In the end, Artificial Intelligence for Simple Games is an excellent course that teaches you how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games, giving you invaluable knowledge and intuition to build AI within a gaming environment and beyond.
