Machine Learning in JavaScript with TensorFlow.js

6h 42m 20s
English
Paid

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 2022. 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.

Read more about the course

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

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# Title Duration
1 Introduction: What is TensorFlow.js? 05:35
2 Course Overview 06:33
3 Machine Learning Concepts 07:58
4 Overview of Artificial Neural Networks 10:09
5 TensorFlow.js environments 04:31
6 Running TensorFlow.js in the browser 05:53
7 WebGL optimisations in TensorFlow.js 05:18
8 Running TensorFlow.js on Node.js 13:42
9 Review 02:02
10 TensorFlow.js APIs 05:19
11 What is a Tensor? 11:44
12 Tensor Math Operations & Ops API 04:03
13 Memory Management in TensorFlow.js 08:32
14 Review 01:53
15 Linear Regression 06:37
16 Reading data from CSV 13:44
17 Visualising the data 07:29
18 Preparing Features and Labels 03:48
19 Normalisation with TensorFlow.js 07:50
20 Splitting into Training and Testing data 10:42
21 Review 01:53
22 Introduction to Layers API 05:37
23 Creating Layers in TensorFlow.js 10:25
24 Inspecting a TensorFlow.js model 05:53
25 Compiling the model 07:38
26 Review 01:38
27 Introduction to Training and Testing 04:33
28 Training with model.fit 05:28
29 Visualising loss with tfjs-vis 11:32
30 Testing with model.evaluate 06:14
31 Training and testing: review & lab 01:28
32 Integrating TensorFlow.js with a UI 19:02
33 Saving and loading a model 12:45
34 Making Predictions 07:48
35 Visualising Predictions 14:36
36 Non-linear Regression 13:38
37 Prediction: review & labs 02:07
38 Introduction: Binary Classification 05:21
39 Visualising Classification Data 19:25
40 Preparing Multiple Features 12:27
41 Binary Classification Model 05:19
42 Visualising Classification with Heatmaps 18:23
43 Binary Classification Predictions 05:14
44 Binary Classification: Review & Lab 02:20
45 Introduction: Multi-class Classification 08:05
46 One hot encoding 09:05
47 Multi-class classification model 04:38
48 Visualising Multi-class Predictions 13:06
49 Multi-class prediction 05:19
50 Multi-class Classification: Review & Lab 02:00
51 Course Review 08:10
52 Next steps with TensorFlow.js 07:51

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