Beginner's Guide to Machine Learning in JavaScript with ml5.js

 Beginner's Guide to Machine Learning in JavaScript with ml5.js - YouTube

https://www.youtube.com/watch?v=26uABexmOX4


Transcript:

(00:00) Hello and welcome to A Beginner's Guide to Machine Learning in JavaScript on-- [TRAIN HORN SOUNDS] --The Coding Train. So this is a learning playlist. What I've done here is taken a whole set of existing videos-- about 19 of them-- and compiled them and organized them, sequenced them, into these six sections.

(00:16) So if you already know how to program in JavaScript-- if you don't, I've got a different learning playlist for you called Code, an Introduction to programming with P5.JS. But if you've already watched that or know the basics in JavaScript, have heard the terms machine learning, maybe, a little intimidated by them-- this seems a little scary, what could you possibly do with that? Then my hope is that these videos will help you get a high-level understanding of core concepts in machine learning and be inspired

(00:39) to apply some of those concepts in creative ways in the browser with JavaScript and the P5JS library. So I've got a whole set of sections here. I'm going to start with pre-trained models. What's the pre-trained model? I will explain that. To do image classification in the browser-- what's in an image, I'll look at something called transfer learning, which allows you to train an image classifier with your own images.

(01:02) Different other algorithms, I'll use something called teachable machine, which is an interface from Google creative lab that allows you to train your machine learning model. And then I will even show you how to train a neural network from scratch with your own data that you've collected. And we'll to do some stuff with audio and sound.

(01:18) So there's a variety of things in here. I hope you'll enjoy it. All aboard. Come with me on the coding train. A Beginner's Guide to Machine Learning in JavaScript. Thanks so much and I look forward to seeing what you create.

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