Artificial Intelligence. The final frontier. For most of us still a book with seven seals. Where should developers start to write their first AI programs? In this course you learn to build Neural Networks and Genetic Algorithms from the ground up. Without frameworks that hide all the interesting stuff in a black box, you are going to build a program that trains self-driving cars. You will learn and assemble all the required building blocks and will be amazed that in no time cars are learning to drive autonomously. There is only one way to learn AI and that is to just pick a project and start building. That is what this course is about!

Target audience

Developers who especially benefit from this course, are:

• developers who want to use their basic Python skills to program self-driving cars.

• developers who want to understand Neural Networks and Genetic Algorithms by building them from the ground up.

   

Challenges

Artificial Intelligence is a black box to many developers. The problem is that many AI frameworks hide the details you need to understand how all the individual components work. The solution is to build things from the ground up and learn to create and combine genetic operators and what properties you can change to optimize the result. This course starts with an empty script and shows you every step that is needed to create autonomous cars that learn how to drive on tracks. Once you have seen the building blocks of a Genetic Algorithm, you can use them in your future projects!

What can you do after this course?

• define what problems can be solved with Genetic Algorithms

• build Neural Networks and Genetic Algorithms from the ground up

• take any problem that can be solved with genetic algorithms and solve it by re-using the code you created in this course

   

Topics

• AI Introduction: Neural Networks and the Genetic Algorithm

• Car mechanics: Creating a window, drawing backgrounds and cars, controlling the car. Understanding track information

• Neural Network: Inputs, outputs, sensors, activation, feed forward

• Genetic Algorithm: Fitness, Chromosomes, Selection, Cross over and Mutation

• Challenges: Slipping cars, Store the car brain, Stay in the middle of the road and Test Drives