CF

Machine Learning in JavaScript with TensorFlow.js

6h 42m 20s
English
Paid
Updated September 2026

Machine Learning in JavaScript with TensorFlow.js is a 52-lesson 6 hours 42 minutes self-paced course by Udemy. TensorFlow.

Course facts

Lessons
52
Duration
6 hours 42 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Udemy
Price
Premium

TensorFlow.js brings machine learning into the browser, which means this 52-lesson course can teach model building without asking you to touch Python. Basic JavaScript is the only prerequisite.

A real dataset, real questions

Rather than toy examples, you work with an actual house-price dataset and answer concrete questions with it: can a model predict the price? Can it tell whether a house has a waterfront? The model gets built piece by piece as the questions get more specific.

Course path

  • TensorFlow.js core concepts, installation, and setup
  • Preparing data and defining a model
  • Training, testing, and generating predictions
  • Binary and multi-class classification

Each part builds directly on the last, and everything runs as a small web demo so you can see model output and test results right in the browser as you go.

Who teaches Machine Learning in JavaScript with TensorFlow.js? Udemy

Udemy thumbnail

Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in Machine Learning in JavaScript with TensorFlow.js?

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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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Frequently asked questions

What prerequisites are needed for this course?
The course requires only basic knowledge of JavaScript. There is no need for prior experience with Machine Learning or TensorFlow.js, as the course starts from simple concepts and builds up your skills with each lesson.
What projects or tasks will I complete during the course?
You will work with real house price data, using it to build and train models that predict prices and classify properties, such as determining whether a house has a waterfront. These tasks are designed to build on each other, helping you understand the process of model creation and testing in a browser environment.
Who is the target audience for this course?
This course is designed for JavaScript developers interested in applying Machine Learning in the browser using TensorFlow.js. It's suitable for those who want to expand their skills into the domain of Machine Learning without delving into Python or other languages traditionally associated with this field.
How does the depth of this course compare to others on Machine Learning?
This course focuses specifically on using TensorFlow.js for Machine Learning in JavaScript, rather than a broad overview of Machine Learning concepts. It provides a practical, hands-on approach to building and testing models in the browser, making it unique among courses that often focus on Python-based implementations.
What specific tools or platforms are used in this course?
The course uses TensorFlow.js, a library that allows Machine Learning models to run directly in the browser or on Node.js. Students will also use WebGL optimizations and explore APIs like the Layers API and Ops API within TensorFlow.js to manage model building and data operations.
What topics are not covered in this course?
The course does not cover Python-based Machine Learning frameworks or advanced data science topics outside the scope of basic model building and testing in TensorFlow.js. It also does not delve into deployment of models beyond browser-based applications.
What is the time commitment required for this course?
The course comprises 52 lessons, each designed to be short and focused. Although the total runtime is listed as 00:00:00, students should expect to spend additional time on hands-on practice and labs, especially when working with data and testing models.