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Artificial Intelligence: Genetic Machine Learning Algorithms
Rating: 3.6 out of 5(31 ratings)
300 students

Artificial Intelligence: Genetic Machine Learning Algorithms

Learn AI based Genetic Algorithms in Machine Learning from theory to hands-on experience by developing 2 apps and game
Created byVinay Phadnis
Last updated 6/2019
English

What you'll learn

  • Create custom Machine Learning models using genetical algorithm to solve new problems
  • You will also be equipped with enough knowledge to create an AI which will play games for you and be better at it as time goes by !

Course content

8 sections • 57 lectures • 8h 10m total length

  • What is Artificial Intelligence or Machine Learning?11:18

    Students will understand the basics of what machine Learning is on a very Fundamental Level. The difference between a Neural network and a genetic algorithm is highlighted here as well. The main reason of distinction between the classical programming approach and the Machine Learning approach is clarified as well!

  • Inspiration for Genetic Algorithms! (Nature)10:31

    Nature! the creator of everyone is one of the biggest sources of inspiration of the creation of genetic algorithms which turned out to be very effective in the domain of Machine Learning. The process of biological evolution is explained in Camels, Beatles and the American Bison.

  • Survival of the 'Fittest'5:03

    The fitness function or the loss function is the most important driver in considering how the program will evolve and solve a particular problem. The fitness function is explained here with the help of some examples as well

  • Elitism4:18
  • Mating5:21

    The most important fundamental concept according to me in Genetic algorithm. This is the stage which is completely responsible for the improving accuracy of any genetic algorithm and then can be extended to get an efficient solution for solving problems

Requirements

  • You don't need to have any prior experience in programming!
  • This course will cover everything from the very basics right from installing the tools required!
  • A keen interest and a mind which is amused by seeing their creation being better and better at solving a task can definitely be called a pre-requisite
  • No need to subscribe to any paid and proprietary software like MATLAB

Description

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 section

  • Genetical 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 game

  • This 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 speed


    I 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

Who this course is for:

  • Anyone excited about machine learning or artificial intelligence
  • Anyone who will be fascinated to be a part of the future where AI does most of the work!

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