Get productive with TensorFlow.js - Google's Machine Learning library for JavaScript. From pre-made off the shelf models to writing or training your own, learn how to create next gen web apps.

Are you a web engineer, designer, or creative thinker looking to apply AI or use Machine Learning in your next web application but are unsure where to begin? Or maybe you’re overwhelmed by other courses that focus more on the mathematical proofs than actually enabling you to use these new technologies for real world applications? This course offers a solution and the knowledge to be the "missing manual" for JavaScript users without a background in Machine Learning.

Machine Learning (ML) on the web is growing faster than ever so now is the time to take your first steps too. Learn what the difference is between Artificial Intelligence, Machine Learning, and Deep Learning but also how to use such techniques practically through real examples using TensorFlow.js - Google's leading ML library for JavaScript.

Supercharge your next web app with superpowers - from classifying text in a blog post comment to automatically block spam, to using sensors like a webcam on your mobile device to alert you when your dog is on the couch after you left the house. The knowledge you learn could be applied to any business OR creative idea you have for your next project no matter what industry you may be working in.

Better yet, JavaScript is one of few programming languages that can run everywhere enabling you to leverage the knowledge from this course and apply it client side, server side, via native apps, and even IoT devices allowing you to reuse what you learn across multiple environments.

?This course aims to educate, inspire, and enable you to rapidly create your next ML powered idea in this rapidly emerging industry while providing you with a solid foundation to understand the field and confidence to explore the industry further.

Web applications are evolving, so sign up, join the fun, and get an edge over the competition. No background in ML is required to take the course. A basic, working knowledge of web technologies such as HTML, CSS, and JavaScript is highly recommended.

Institution: Google

Subject: Computer Science

Level: Introductory

Prerequisites:

None

Associated programs:

Professional Certificate in Fundamentals of Google AI for Web Based Machine Learning

Language: English

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Associated skills: Web Applications, Server-Side, Machine Learning, Web Engineering, Basic Math, Deep Learning, HyperText Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript (Programming Language), Internet Of Things (IoT), Artificial Intelligence

What you'll learn

Common terms and what they mean

How Machine Learning works (without formal mathematical definitions)

Overview of the TensorFlow.js library

Advantages of using ML in JavaScript

Ways to consume or create Machine Learning models

How to use pre-made “off the shelf” models

What Tensors are in Machine Learning

How to use Tensors with ML models

How to write a simple custom model

Perceptrons (artificial neuron) and how they work

Linear regression to predict numbers using single neuron

Multi layered perceptrons for handling more complex data

How to use models that use Convolutional Neural Networks for images

How to convert Python models to JavaScript

Transfer learning - reusing existing trained models with your own data

Inspiring projects others are creating to seed your own future ideas