Genetic Algorithm for Machine Learning

[LeeAndro]

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

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.

Simplified Way to 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

No

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




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