Artificial Intelligence: Genetic Machine Learning Algorithms

About this course

In this course we will be focusing on learning Genetical Algorithms used in machine learning in the following modules:Theory: This section will consider the basics of what Machine Learning actually is at its very fundamental level also followed by its difference with classical programming of defining rules beforehand. The main differences between a Neural Network and Genetical Algorithm are also highlighted into this sectionGenetical Algorithm: The basic concepts are taken care of over here starting from the basics like a fitness function which as I like to call it, a major driver into the direction of learning or output that your program will eventually take up. Elitism, followed by Mating or crossover or mutation which are the key factors responsible for the 'learning' in machine learning are explained well in detail over here.Guess-the-phrase: This is our first programming project based on Python. It is a light-weight project which serves a good purpose of providing clarity into the various aspects of Genetical Algorithm.Path-Finder: This will be our second project which will use the concepts initialised in the first project to a new depth. This will be our first project where we will be having some graphical (non-terminal) output.Flappy Bird: A JavaScript Flappy Bird  will be created which used genetic algorithm to simulate multiple players and use neural network to play the gameThis course is created by keeping absolute beginners in mind. If you are a professional and find the course to be a bit slower. You can always view the lectures at 2x speedI hope you take away something useful from this course and use it to create awesome new programs which in turn will be your contribution in making the world a better place

Course objectives

• introduce the fundamentals of genetic algorithms
• demonstrate practical applications through projects
• differentiate between genetic algorithms and neural networks