A Quick Start Guide to Genetic Algorithms in Python

What you'll 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

Requirements
A basic understanding of computer programming preferably in Python

Description
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

Who this course is for
Any one having interest in Artificial Intelligence, Machine Learning, Evolutionary Computing and Heuristic based Optimization

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