A Quick Start Guide to Genetic Algorithms in Python

A Quick Start Guide to Genetic Algorithms in Python, available at $64.99, has an average rating of 4.25, with 16 lectures, 1 quizzes, based on 22 reviews, and has 176 subscribers.

You will learn about Understand the reasons behind using Genetic Algorithm (GA) and knows in which situations GA can be used Can explain GA and Can identify problem types which might require GAs to find a solution Grasp working principles of GA and Understand how crossover, mutation and selection operators work Develop skills in using GA as an optimisation technique Have an idea about GA implementations using Python and Python Libraries for GA Utilise GA for the optimisation problems like 8-Queen Problem and Knapsack problem This course is ideal for individuals who are Any one having interest in Artificial Intelligence, Machine Learning, Evolutionary Computing and Heuristic based Optimization It is particularly useful for Any one having interest in Artificial Intelligence, Machine Learning, Evolutionary Computing and Heuristic based Optimization.

Enroll now: A Quick Start Guide to Genetic Algorithms in Python

Summary

Title: A Quick Start Guide to Genetic Algorithms in Python

Price: $64.99

Average Rating: 4.25

Number of Lectures: 16

Number of Quizzes: 1

Number of Published Lectures: 16

Number of Published Quizzes: 1

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: ₹2,999

Quality Status: approved

Status: Live

What You Will Learn

Understand the reasons behind using Genetic Algorithm (GA) and knows in which situations GA can be used
Can explain GA and Can identify problem types which might require GAs to find a solution
Grasp working principles of GA and Understand how crossover, mutation and selection operators work
Develop skills in using GA as an optimisation technique
Have an idea about GA implementations using Python and Python Libraries for GA
Utilise GA for the optimisation problems like 8-Queen Problem and Knapsack problem

Who Should Attend

Any one having interest in Artificial Intelligence, Machine Learning, Evolutionary Computing and Heuristic based Optimization

Target Audiences

Any one having interest in Artificial Intelligence, Machine Learning, Evolutionary Computing and Heuristic based Optimization

Get ready to enhance your career profile with upgraded skill in Genetic Algorithm

Get ready to apply Genetic Algorithm to practical optimization problem quickly

Get ready to implement Genetic Algorithm in Python / Python Library quickly

ENROLL FOR THE COURSE NOW IF:

· You want to quickly Learn Genetic Algorithm to solve AI & ML problems.

· You want to quickly master the GA based solutions to optimization problems.

· You want to quickly develop GA based applications in Python.

WHAT’S IN THE COURSE?

· Approximately Three Hours of video content including

· Quick Introduction to Genetic Algorithm with Examples

· Four applications of Genetic Algorithm completely implemented in Python

· Four assignments on developing application of Genetic Algorithm using Python.

COURSE STRUCTURE:

This course is designed such that it can be completed in minimal time with the maximum outcome. The course is divided into Eleven sections namely

(i) GA Flow Diagram, (ii) GA Biological Analogy, (iii) GA Essential Five Phases,

(iv) GA Calculations- Diophantine Equation, (v). GA Diophantine Equation – Python Implemented,

(vi) GA Application- Message Generation (Password Cracking), (vii) GA Python Libraries,

(viii) GA Application- Knapsack Problem, (ix) GA Application- Eight Queen Problem,

(x) GA Issues and Application Types and (xi) GA Quiz with Issues before GA Practitioner.

Each of these sections will help you learn and master the Genetic Algorithm with ease, providing you with the knowledge about various steps which are essential to successfully complete any optimization project.

WHAT DO YOU GET AFTER YOU ENROLL FOR THIS COURSE?

· Lifetime access to the content of this course

· Grasp of the Five-Phases of Genetic Algorithm with application development to AI/ML problems.

· Master the essential skills for implementation of Genetic Algorithm using Python and Python Library

WHY TAKE THIS COURSE?

Each of the lectures is designed such that the learner can get a clear understanding of all the steps in the genetic algorithm without involving unnecessary mathematical complexities. Four practical applications have been demonstrated step-by-step either hand-coded or by using Python / Python Library. The same problems are assigned as practice exercise to crystalize the practical implementation of Genetic Algorithm to optimization problems from the domain of AI. The Annotated GA Quiz Show shall help the learner to review the understanding of the material presented.

OUTCOMES OF THE COURSE:

UNDERSTAND the Genetic Algorithm viz-a-viz traditional conventional algorithms.

KNOW the Essential Five Phases of the Genetic Algorithm.

LEARN to implement the Genetic Algorithm using Python and Python Library.

IDENTIFY the problem domains to apply the Genetic Algorithm.

So why wait? Enroll Now!!!

Albert Einstein said, “Everything must be made as simple as possible, but not simpler”.

This course aims at introducing the GA in simple and precise way without unnecessary mathematical complexity. It focuses mainly on bringing the genetic algorithm concepts home in simplest possible manner. The contents are explained in simplest possible manner such that anyone interested in learning the application of GA can practice the given examples without much ado. Each of the lecture videos is short, precise and focuses on single idea. The practical GA phases are introduced right at the beginning along with practice example. The minimum required theory is covered in the middle of the course. This course aims at introducing the learner to working of GA taking him/her from known-to-unknown. GA is evolutionary algorithm; the lesson plan of the GA module here has been designed to support evolutionary learning.

Just-In-Time Learning with Just-In-Time Teaching of Just-What-Is-Required.

What is GA: Evolutionary Optimizing Algorithm

Why GA: Small, Simple and Effective

How GA: Five Simple Phases

When GA: Large Solution Space

Where GA: Artificial Intelligence and Machine Learning

Course Curriculum

Chapter 1: Prologue

Lecture 1: Introduction

Lecture 2: Module Outline

Lecture 3: Biological Analogy

Chapter 2: Application 01: Diophantine Equation

Lecture 1: Details of Genetic Algorithm

Lecture 2: Hand Coding the Diophantine Equation

Lecture 3: Diophantine App

Lecture 4: Implementing Diophantine Equation in Python

Chapter 3: Theory of Genetic Algorithm

Lecture 1: Algorithmics

Lecture 2: Search Space

Lecture 3: Biological Background of Genetic Algorithm

Chapter 4: Application 02: Message Generation

Lecture 1: Message Generation App in Python

Chapter 5: Application 03: Knapsack Problem

Lecture 1: PyEasyGA – Python's Library for Genetic Algorithm

Lecture 2: Knapsack Problem App

Chapter 6: Application 04: The 8-Queen Problem

Lecture 1: The 8-Queen Problem – Statement

Lecture 2: The 8-Queen Problem – Solution

Chapter 7: Epilogue

Lecture 1: Conclusion – Application Domains of GA and Issues

Instructors

Dr. M. S. Ali
Engineer by Training, Teacher by Profession

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 7 votes
5 stars: 12 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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