Machine Learning in JavaScript with TensorFlow.js

6h 42m 20s
English
Paid

Course description

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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#1: Introduction: What is TensorFlow.js?

All Course Lessons (52)

#Lesson TitleDurationAccess
1
Introduction: What is TensorFlow.js? Demo
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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