Genetic Algorithm for Machine Learning
Created by Parteek Bhatia | Last updated 1/2021
Duration: 1h 32m | 1 section | 8 lectures | Video: 1280x720, 44 KHz | 488 MB
Genre: eLearning | Language: English + Sub

Simplified Way to Learn

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

Requirements
No
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 CourseMachine Learning EnthusiastStudents preparing for placement tests and interviews