Genetic Algorithm for Machine Learning

Genetic Algorithm for Machine Learning, available at Free, has an average rating of 4.35, with 8 lectures, based on 52 reviews, and has 3346 subscribers.

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You will learn about 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 This course is ideal for individuals who are Students taking Genetics Algorithm or Machine Learning or Artificial Intelligence Course or Machine Learning Enthusiast or Students preparing for placement tests and interviews It is particularly useful for Students taking Genetics Algorithm or Machine Learning or Artificial Intelligence Course or Machine Learning Enthusiast or Students preparing for placement tests and interviews.

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Summary

Title: Genetic Algorithm for Machine Learning

Price: Free

Average Rating: 4.35

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Students taking Genetics Algorithm or Machine Learning or Artificial Intelligence Course

Machine Learning Enthusiast

Students preparing for placement tests and interviews

Target Audiences

Students taking Genetics Algorithm or Machine Learning or Artificial Intelligence Course

Machine Learning Enthusiast

Students preparing for placement tests and interviews

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.

Course Curriculum

Chapter 1: Genetics Algorithm

Lecture 1: Concept of Genetic Algorithm

Lecture 2: Key terms used in GA & Its Mathematical Representation

Lecture 3: Implementation of Natural Selection: Part-1

Lecture 4: Implementation of Natural Selection: Part-2

Lecture 5: Implementation of Recombination: Part-1

Lecture 6: Implementation of Recombination: Part-2

Lecture 7: Implementation of Mutation

Lecture 8: Concept and Implementation of Elitism

Instructors

Parteek Bhatia
Professor, CSED, TIET, Patiala, India

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 8 votes

4 stars: 19 votes

5 stars: 22 votes

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