Artificial Intelligence- Genetic Machine Learning Algorithms

Artificial Intelligence: Genetic Machine Learning Algorithms, available at $49.99, has an average rating of 3.35, with 57 lectures, based on 31 reviews, and has 300 subscribers.

You will learn about 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 ! This course is ideal for individuals who are Anyone excited about machine learning or artificial intelligence or Anyone who will be fascinated to be a part of the future where AI does most of the work! It is particularly useful for Anyone excited about machine learning or artificial intelligence or Anyone who will be fascinated to be a part of the future where AI does most of the work!.

Enroll now: Artificial Intelligence: Genetic Machine Learning Algorithms

Summary

Title: Artificial Intelligence: Genetic Machine Learning Algorithms

Price: $49.99

Average Rating: 3.35

Number of Lectures: 57

Number of Published Lectures: 57

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 57

Original Price: ?7,900

Quality Status: approved

Status: Live

What You Will 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 !

Who Should Attend

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!

Target Audiences

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!

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Artificial Intelligence or Machine Learning?

Lecture 2: Inspiration for Genetic Algorithms! (Nature)

Lecture 3: Survival of the Fittest

Lecture 4: Elitism

Lecture 5: Mating

Chapter 2: Game#1: Guess The Phrase (Part-1)

Lecture 1: Installing Python and pip

Lecture 2: Introduction about the project

Lecture 3: Main method

Lecture 4: Individual Class and init method

Lecture 5: Calculate Fitness method

Lecture 6: Create new Gnome

Lecture 7: Mutating Genes

Lecture 8: Running the Program!

Chapter 3: Guess the Phrase (Part-2)

Lecture 1: Adding the AI

Lecture 2: Implementing the Loop

Lecture 3: Mating process (creation of new offsprings)

Lecture 4: Completing the Code

Lecture 5: Running the Program (Bonus Debugging as well)

Lecture 6: Cleaning Up the Print statement

Chapter 4: Find The Shortest Path (Part-1)

Lecture 1: Introduction about the project

Lecture 2: Initialising an individual

Lecture 3: Convert DNA to lines on Graph

Lecture 4: Fitness function

Lecture 5: Genetical Evolution

Lecture 6: Implement Cross-over

Lecture 7: Add Mutation

Chapter 5: Find The Shortest Path (Part-2)

Lecture 1: Creating a Line Object

Lecture 2: Plotting using matplotlib

Lecture 3: Adding the Entry Point

Lecture 4: Running the Program (Bonus Debugging as well)

Chapter 6: Flappy Bird game

Lecture 1: Project Intro

Lecture 2: index.html

Lecture 3: Adding minified js files

Lecture 4: Adding assets

Lecture 5: Importing assets

Lecture 6: Setting Gravity

Chapter 7: Genetic.js of Flappy Bird

Lecture 1: What is Genetic Optimization ?

Lecture 2: Genetics

Lecture 3: Create Population

Lecture 4: Activate Brain

Lecture 5: Evolve Population

Lecture 6: Selection

Lecture 7: Mutating Genes

Lecture 8: Finishing Genetic.js

Chapter 8: Back to the Gameplay.js

Lecture 1: Creating a bird object

Lecture 2: Creating a Tree Object

Lecture 3: Creating a Tree Group

Lecture 4: Creating a Bird Group

Lecture 5: Bitmap Objects

Lecture 6: Adding Buttons

Lecture 7: Update switch case

Lecture 8: Add collisions

Lecture 9: Reset the Background

Lecture 10: More methods!

Lecture 11: Completing the Code

Lecture 12: Debugging and Running the code

Lecture 13: BONUS lecture

Instructors

Vinay Phadnis
CTO, Machine Learning & Quantum Consultant

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 7 votes

4 stars: 9 votes

5 stars: 11 votes

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