Artificial Intelligence: Genetic Machine Learning Algorithms
Artificial Intelligence: Genetic Machine Learning Algorithms, available at $49.99, has an average rating of 3.35, with 57 lectures, based on 31 reviews, and has 300 subscribers.
You will learn about Create custom Machine Learning models using genetical algorithm to solve new problems You will also be equipped with enough knowledge to create an AI which will play games for you and be better at it as time goes by ! This course is ideal for individuals who are Anyone excited about machine learning or artificial intelligence or Anyone who will be fascinated to be a part of the future where AI does most of the work! It is particularly useful for Anyone excited about machine learning or artificial intelligence or Anyone who will be fascinated to be a part of the future where AI does most of the work!.
Enroll now: Artificial Intelligence: Genetic Machine Learning Algorithms
Summary
Title: Artificial Intelligence: Genetic Machine Learning Algorithms
Price: $49.99
Average Rating: 3.35
Number of Lectures: 57
Number of Published Lectures: 57
Number of Curriculum Items: 57
Number of Published Curriculum Objects: 57
Original Price: ₹7,900
Quality Status: approved
Status: Live
What You Will Learn
- Create custom Machine Learning models using genetical algorithm to solve new problems
- You will also be equipped with enough knowledge to create an AI which will play games for you and be better at it as time goes by !
Who Should Attend
- Anyone excited about machine learning or artificial intelligence
- Anyone who will be fascinated to be a part of the future where AI does most of the work!
Target Audiences
- Anyone excited about machine learning or artificial intelligence
- Anyone who will be fascinated to be a part of the future where AI does most of the work!
In this course we will be focusing on learning Genetical Algorithms used in machine learning in the following modules:
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Theory: This section will consider the basics of what Machine Learning actually is at its very fundamental level also followed by its difference with classical programming of defining rules beforehand. The main differences between a Neural Network and Genetical Algorithm are also highlighted into this section
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Genetical Algorithm: The basic concepts are taken care of over here starting from the basics like a fitness function which as I like to call it, a major driver into the direction of learning or output that your program will eventually take up. Elitism, followed by Mating or crossover or mutation which are the key factors responsible for the ‘learning’ in machine learning are explained well in detail over here.
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Guess-the-phrase: This is our first programming project based on Python. It is a light-weight project which serves a good purpose of providing clarity into the various aspects of Genetical Algorithm.
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Path-Finder: This will be our second project which will use the concepts initialised in the first project to a new depth. This will be our first project where we will be having some graphical (non-terminal) output.
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Flappy Bird: A JavaScript Flappy Bird will be created which used genetic algorithm to simulate multiple players and use neural network to play the game
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This course is created by keeping absolute beginners in mind. If you are a professional and find the course to be a bit slower. You can always view the lectures at 2x speed
I hope you take away something useful from this course and use it to create awesome new programs which in turn will be your contribution in making the world a better place
Course Curriculum
Chapter 1: Introduction
Lecture 1: What is Artificial Intelligence or Machine Learning?
Lecture 2: Inspiration for Genetic Algorithms! (Nature)
Lecture 3: Survival of the 'Fittest'
Lecture 4: Elitism
Lecture 5: Mating
Chapter 2: Game#1: Guess The Phrase (Part-1)
Lecture 1: Installing Python and pip
Lecture 2: Introduction about the project
Lecture 3: Main method
Lecture 4: Individual Class and init method
Lecture 5: Calculate Fitness method
Lecture 6: Create new Gnome
Lecture 7: Mutating Genes
Lecture 8: Running the Program!
Chapter 3: Guess the Phrase (Part-2)
Lecture 1: Adding the AI
Lecture 2: Implementing the Loop
Lecture 3: Mating process (creation of new offsprings)
Lecture 4: Completing the Code
Lecture 5: Running the Program (Bonus Debugging as well)
Lecture 6: Cleaning Up the Print statement
Chapter 4: Find The Shortest Path (Part-1)
Lecture 1: Introduction about the project
Lecture 2: Initialising an individual
Lecture 3: Convert DNA to lines on Graph
Lecture 4: Fitness function
Lecture 5: Genetical Evolution
Lecture 6: Implement Cross-over
Lecture 7: Add Mutation
Chapter 5: Find The Shortest Path (Part-2)
Lecture 1: Creating a Line Object
Lecture 2: Plotting using matplotlib
Lecture 3: Adding the Entry Point
Lecture 4: Running the Program (Bonus Debugging as well)
Chapter 6: Flappy Bird game
Lecture 1: Project Intro
Lecture 2: index.html
Lecture 3: Adding minified js files
Lecture 4: Adding assets
Lecture 5: Importing assets
Lecture 6: Setting Gravity
Chapter 7: Genetic.js of Flappy Bird
Lecture 1: What is Genetic Optimization ?
Lecture 2: Genetics
Lecture 3: Create Population
Lecture 4: Activate Brain
Lecture 5: Evolve Population
Lecture 6: Selection
Lecture 7: Mutating Genes
Lecture 8: Finishing Genetic.js
Chapter 8: Back to the Gameplay.js
Lecture 1: Creating a bird object
Lecture 2: Creating a Tree Object
Lecture 3: Creating a Tree Group
Lecture 4: Creating a Bird Group
Lecture 5: Bitmap Objects
Lecture 6: Adding Buttons
Lecture 7: Update switch case
Lecture 8: Add collisions
Lecture 9: Reset the Background
Lecture 10: More methods!
Lecture 11: Completing the Code
Lecture 12: Debugging and Running the code
Lecture 13: BONUS lecture
Instructors
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Vinay Phadnis
CTO, Machine Learning & Quantum Consultant
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 2 votes
- 3 stars: 7 votes
- 4 stars: 9 votes
- 5 stars: 11 votes
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