Machine Learning in JavaScript with TensorFlow.js

Machine Learning in JavaScript with TensorFlow.js, available at $119.99, has an average rating of 4.46, with 75 lectures, 9 quizzes, based on 756 reviews, and has 5427 subscribers.

You will learn about Machine Learning in Javascript and TensorFlowJS 3 Deep Learning and Neural Network concepts Why TensorFlow for JavaScript is a game changer Defining machine learning models How to install and run TensorFlowJS 3 How TensorFlowJS 3 is optimised Training machine learning models Data preparation for machine learning How to make accurate predictions Linear regression Binary classification Multi-class classification Heatmap visualisation Scatter-plot visualisation Importing and normalising data How to manage memory in TensorFlowJS 3 Tensor mathematics Saving machine learning models Inputting and outputting using a web browser Javascript and machine learning integration Shuffling, and splitting data In-depth labs for practical development This course is ideal for individuals who are Anyone who wants to start using machine learning in their apps and websites using Javascript It is particularly useful for Anyone who wants to start using machine learning in their apps and websites using Javascript.

Enroll now: Machine Learning in JavaScript with TensorFlow.js

Summary

Title: Machine Learning in JavaScript with TensorFlow.js

Price: $119.99

Average Rating: 4.46

Number of Lectures: 75

Number of Quizzes: 9

Number of Published Lectures: 75

Number of Published Quizzes: 9

Number of Curriculum Items: 84

Number of Published Curriculum Objects: 84

Original Price: ￡199.99

Quality Status: approved

Status: Live

What You Will Learn

Machine Learning in Javascript and TensorFlowJS 3

Deep Learning and Neural Network concepts

Why TensorFlow for JavaScript is a game changer

Defining machine learning models

How to install and run TensorFlowJS 3

How TensorFlowJS 3 is optimised

Training machine learning models

Data preparation for machine learning

How to make accurate predictions

Linear regression

Binary classification

Multi-class classification

Heatmap visualisation

Scatter-plot visualisation

Importing and normalising data

How to manage memory in TensorFlowJS 3

Tensor mathematics

Saving machine learning models

Inputting and outputting using a web browser

Javascript and machine learning integration

Shuffling, and splitting data

In-depth labs for practical development

Who Should Attend

Anyone who wants to start using machine learning in their apps and websites using Javascript

Target Audiences

Anyone who wants to start using machine learning in their apps and websites using Javascript

Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you!

This is the tutorial you’ve been looking for to become a modern JavaScript machine learning master in 2024.It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master – join the TensorFlow.js revolution.

This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.

Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.

Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.

This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:

Part 1 – Introduction to TensorFlow.js

Part 2 – Installing and running TensorFlow.js

Part 3 – TensorFlow.js Core Concepts

Part 4 – Data Preparation with TensorFlow.js

Part 5 – Defining a model

Part 6 – Training and Testing in TensorFlow.js

Part 7 – TensorFlow.js Prediction

Part 8 – Binary Classification

Part 9 – Multi-class Classification

Part 10 – Conclusion & Next Steps

As a bonus, for every student, we provide you with JavaScript and HTML code templates that you can download and use on your own projects.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction: What is TensorFlow.js?

Lecture 2: Course Overview

Lecture 3: Machine Learning Concepts

Lecture 4: Overview of Artificial Neural Networks

Lecture 5: Lab: TensorFlow Playground

Lecture 6: Summary

Chapter 2: Installing and running TensorFlow.js

Lecture 1: TensorFlow.js environments

Lecture 2: Running TensorFlow.js in the browser

Lecture 3: WebGL optimisations in TensorFlow.js

Lecture 4: Running TensorFlow.js on Node.js

Lecture 5: New: TensorFlow.js for React Native

Lecture 6: Review

Lecture 7: Lab: Install and run TensorFlow.js in the browser

Lecture 8: Lab: Install and run TensorFlow.js on Node.js

Lecture 9: Summary

Chapter 3: TensorFlow.js Core Concepts

Lecture 1: TensorFlow.js APIs

Lecture 2: What is a Tensor?

Lecture 3: Tensor Math Operations & Ops API

Lecture 4: Memory Management in TensorFlow.js

Lecture 5: Review

Lecture 6: Lab: Tensor Math and Memory Management

Lecture 7: Summary

Chapter 4: Data Preparation with TensorFlow.js

Lecture 1: Linear Regression

Lecture 2: Reading data from CSV

Lecture 3: Visualising the data

Lecture 4: Preparing Features and Labels

Lecture 5: Normalisation with TensorFlow.js

Lecture 6: Splitting into Training and Testing data

Lecture 7: Review

Lecture 8: Lab: Prepare the Data

Lecture 9: Summary

Chapter 5: Defining a model

Lecture 1: Introduction to Layers API

Lecture 2: Creating Layers in TensorFlow.js

Lecture 3: Inspecting a TensorFlow.js model

Lecture 4: Compiling the model

Lecture 5: Review

Lecture 6: Lab: Creating a Model

Lecture 7: Summary

Chapter 6: Training and Testing in TensorFlow.js

Lecture 1: Introduction to Training and Testing

Lecture 2: Training with model.fit

Lecture 3: Visualising loss with tfjs-vis

Lecture 4: Testing with model.evaluate

Lecture 5: Training and testing: review & lab

Lecture 6: Lab: TensorFlow.js Training and Testing

Lecture 7: Summary

Chapter 7: TensorFlow.js Prediction

Lecture 1: Integrating TensorFlow.js with a UI

Lecture 2: Saving and loading a model

Lecture 3: Making Predictions

Lecture 4: Visualising Predictions

Lecture 5: Non-linear Regression

Lecture 6: Prediction: review & labs

Lecture 7: Lab: TensorFlow.js predictions

Lecture 8: Lab: Beyond Linear Regression

Lecture 9: Lab (optional): Training without Layers API

Lecture 10: Summary

Chapter 8: Binary Classification

Lecture 1: Introduction: Binary Classification

Lecture 2: Visualising Classification Data

Lecture 3: Preparing Multiple Features

Lecture 4: Binary Classification Model

Lecture 5: Visualising Classification with Heatmaps

Lecture 6: Binary Classification Predictions

Lecture 7: Binary Classification: Review & Lab

Lecture 8: Lab: TensorFlow.js Binary Classification

Lecture 9: Summary

Chapter 9: Multi-class Classification

Lecture 1: Introduction: Multi-class Classification

Lecture 2: One hot encoding

Lecture 3: Multi-class classification model

Lecture 4: Visualising Multi-class Predictions

Lecture 5: Multi-class prediction

Lecture 6: Multi-class Classification: Review & Lab

Lecture 7: Lab: TensorFlow.js Multi-class Classification

Lecture 8: Summary

Chapter 10: Conclusion & Next Steps

Lecture 1: Course Review

Lecture 2: Next steps with TensorFlow.js

Lecture 3: Resources for going deeper with TensorFlow.js

Instructors

tech.courses team
Learn by Doing – Technical Courses, Professionally Delivered

Justin Emery
JavaScript / Machine Learning Engineer

Rating Distribution

1 stars: 10 votes

2 stars: 17 votes

3 stars: 100 votes

4 stars: 255 votes

5 stars: 374 votes

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