Genetic Algorithm for Machine Learning

Name: Genetic Algorithm for Machine Learning
Rating: 4.4 (63 reviews)

Simplified Way to Learn

Created byParteek Bhatia

Last updated 1/2021

English

What you'll learn

Working Principle of Genetics Algorithms
Natural Selection
Implementation of Natural Selection through Roulette Wheel
Crossover or Recombination
Concept of Probability of Crossover and Its usage in generation of Population
Mutation
Concept of Probability of Mutation and Its usage in generation of new features
Concept and Implementation of Elitism

Course content

1 section • 8 lectures • 1h 32m total length

Concept of Genetic Algorithm13:44
Genetic algorithm for machine learning explores natural selection, crossover, and mutation to optimize solutions and select global minima from a population of candidates.
Key terms used in GA & Its Mathematical Representation10:02
Implementation of Natural Selection: Part-19:40
Implementation of Natural Selection: Part-211:08
Implementation of Recombination: Part-19:08
Implementation of Recombination: Part-212:56
Implementation of Mutation10:55
Concept and Implementation of Elitism14:33

Requirements

Description

This course covers the working Principle of Genetics Algorithms and its various components like Natural Selection, Crossover or Recombination, Mutation and Elitism in a a very simplified way.

GA are inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

Who this course is for:

Students taking Genetics Algorithm or Machine Learning or Artificial Intelligence Course
Machine Learning Enthusiast
Students preparing for placement tests and interviews