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.
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.
Enroll now: Genetic Algorithm for Machine Learning
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
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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