Artificial Intelligence: Genetic Machine Learning Algorithms (Udemy.com)

( 4 Reviews )

Created by: Vinay Phadnis

Produced in 2021

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Overall Score : 76 / 100

Course Description

In this course we will be focusing on learning Genetical Algorithms used in machine learning in the following modules:

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
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.
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.
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.
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
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

Who this course is for:

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!

Time: 8h

Instructor Details

Vinay Phadnis

3.8 Rating
4 Reviews

I am a freelance programmer by profession and contributor to many open source projects. Extensive experience in -Technologies: Artificial Intelligence, Machine Learning, Blockchain, Quantum Computing, Decentralisation, Mathematical Modelling, Data AnalysisFrameworks: TensorFlow, Cirq, Flutter, Genetic Evolutionary AlgorithmsLanguages: Python, Java, Dart, JavaScript, Go, Shell Scripting Conducted many training programmes on new and upcoming technologies. My speciality is in breaking any seemingly complex topic into basic building blocks along with correlation to real life scenarios. You will inherently find me using the word Basically while explaining concepts :) I take special interest in Quantum Computing, Machine Learning as prominent future technologies and I am amazed by what role AI has already taken in our lives.I take inspiration from frameworks and patterns found in nature. I consider nature as our best teacher for learning the next evolution of computing. I invite you on this journey of exploring and sharing the fascinating world of computing.
Happy Coding and Enjoy Learning !VInay Phadnis

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Reviews

3.8

4 total reviews

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By Curtis Bard

Pretty good. I am familiar with some programming, so I can catch on. If I didn't know programming though, I think this would be tough to follow at times.

I do feel like explanation can be lacking at points. The instructor will often say "here" as a substitute for explanation. I don't get a clear understanding of what the program is doing.

However, the value of what the instructor is showing is quite high. He is providing material that is extremely helpful.

By Anuj Mokashi

Excellent course to start working with machine learning!

By Debmalya Pramanik

Code Implementation is not explained properly and mathematical intuition behind the coding is not explained at all... also the coding style is explained poorly.

By William Richter

Pace too slow. Turned up to 2x, to improve the pace. Only issue was motion sickness as instructor jumping back and forth through the code.

This course seems to be focused more explaining Python/JS and debugging than a Machine Learning/Genetics algorithms course. As I followed along, attempted to catch and fix many of the self induced errors as possible. A large percentage that would be caught or at least questioned in a language aware editor with auto-completion editor like the instructor used. We use editing tools like this to avoid errors that were induced.