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Development Data Science Machine Learning

Machine Learning in JavaScript with TensorFlow.js

Master machine learning with JavaScript and TensorFlowJS. Add artificial intelligence to websites, Node.js and web apps!
Bestseller
Rating: 4.4 out of 54.4 (400 ratings)
2,680 students
Created by tech.courses team, Justin Emery
Last updated 1/2021
English
English
30-Day Money-Back Guarantee

What you'll learn

  • Machine Learning in Javascript and TensorFlowJS 2.0
  • Deep Learning and Neural Network concepts
  • Why TensorFlow for JavaScript is a game changer
  • Defining machine learning models
  • How to install and run TensorFlowJS 2.0
  • How TensorFlowJS 2.0 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 2.0
  • 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
Curated for the Udemy for Business collection

Course content

10 sections • 75 lectures • 7h 15m total length

  • Preview05:34
  • Preview06:32
  • Machine Learning Concepts
    07:57
  • Overview of Artificial Neural Networks
    10:08
  • Lab: TensorFlow Playground
    01:11
  • TensorFlow.js and Machine Learning
    3 questions
  • Summary
    01:36

  • TensorFlow.js environments
    04:30
  • Running TensorFlow.js in the browser
    05:52
  • WebGL optimisations in TensorFlow.js
    05:17
  • Running TensorFlow.js on Node.js
    13:41
  • New: TensorFlow.js for React Native
    00:33
  • Review
    02:01
  • Lab: Install and run TensorFlow.js in the browser
    01:05
  • Lab: Install and run TensorFlow.js on Node.js
    01:40
  • TensorFlow environments and installation
    3 questions
  • Summary
    01:24

  • TensorFlow.js APIs
    05:18
  • What is a Tensor?
    11:43
  • Tensor Math Operations & Ops API
    04:02
  • Memory Management in TensorFlow.js
    08:31
  • Review
    01:52
  • Lab: Tensor Math and Memory Management
    02:04
  • TensorFlow.js math and memory
    5 questions
  • Summary
    00:32

  • Linear Regression
    06:35
  • Reading data from CSV
    13:43
  • Preview07:28
  • Preparing Features and Labels
    03:47
  • Normalisation with TensorFlow.js
    07:49
  • Splitting into Training and Testing data
    10:41
  • Review
    01:52
  • Lab: Prepare the Data
    03:43
  • Data Preparation
    3 questions
  • Summary
    00:29

  • Introduction to Layers API
    05:36
  • Creating Layers in TensorFlow.js
    10:24
  • Inspecting a TensorFlow.js model
    05:52
  • Compiling the model
    07:37
  • Review
    01:37
  • Lab: Creating a Model
    01:18
  • TensorFlow.js Models
    7 questions
  • Summary
    01:07

  • Preview04:32
  • Training with model.fit
    05:27
  • Visualising loss with tfjs-vis
    11:31
  • Testing with model.evaluate
    06:13
  • Training and testing: review & lab
    01:27
  • Lab: TensorFlow.js Training and Testing
    00:48
  • Training and Testing
    4 questions
  • Summary
    00:25

  • Integrating TensorFlow.js with a UI
    19:01
  • Saving and loading a model
    12:44
  • Making Predictions
    07:47
  • Visualising Predictions
    14:35
  • Non-linear Regression
    13:37
  • Prediction: review & labs
    02:06
  • Lab: TensorFlow.js predictions
    04:40
  • TensorFlow.js predictions
    5 questions
  • Lab: Beyond Linear Regression
    00:48
  • Lab (optional): Training without Layers API
    00:52
  • Summary
    00:47

  • Introduction: Binary Classification
    05:20
  • Visualising Classification Data
    19:24
  • Preparing Multiple Features
    12:26
  • Binary Classification Model
    05:18
  • Visualising Classification with Heatmaps
    18:22
  • Preview05:13
  • Binary Classification: Review & Lab
    02:18
  • Lab: TensorFlow.js Binary Classification
    04:37
  • Quiz: Binary Classification
    4 questions
  • Summary
    00:50

  • Introduction: Multi-class Classification
    08:04
  • One hot encoding
    09:04
  • Multi-class classification model
    04:37
  • Visualising Multi-class Predictions
    13:05
  • Multi-class prediction
    05:18
  • Multi-class Classification: Review & Lab
    01:59
  • Lab: TensorFlow.js Multi-class Classification
    01:46
  • Multi-class Classification
    3 questions
  • Summary
    00:22

  • Course Review
    08:09
  • Next steps with TensorFlow.js
    07:50
  • Resources for going deeper with TensorFlow.js
    01:30

Requirements

  • Javascript basics
  • Some high school maths (but we give links if you need a refresher!)

Description

Updated for 2020!

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 2020. 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.

Who this course is for:

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

Instructors

tech.courses team
Learn by Doing - Technical Courses, Professionally Delivered
tech.courses team
  • 4.4 Instructor Rating
  • 400 Reviews
  • 2,680 Students
  • 1 Course

Welcome to the tech.courses community, we look forward to providing massive value to you as you learn-by-doing with the best courses and instructors in the world.

Our courses cover state-of-the-art technology and software development topics. All instructors are experienced industry professionals who want to share their expertise!

See you inside,


John, Managing Director






Justin Emery
JavaScript / Machine Learning Engineer
Justin Emery
  • 4.4 Instructor Rating
  • 400 Reviews
  • 2,680 Students
  • 1 Course

Professional Experience

I have been working as a software developer since 2007. I started out at Feral Interactive, a Mac & Linux video game publisher, building information systems to support all areas of the company. From 2011, I led a team responsible for online services, internal information systems, and the public website. I led development of Calico, an online multiplayer gaming platform.

Since 2017, I have been working freelance on a range of software development and technical consulting projects for startups, from backend APIs to support mobile apps, to a chatbot utilising state-of-the-art machine learning techniques.

Education

I've always looked to invest in the best education.

I graduated with 1st class honours from The University of Manchester in BSc Computer Science.

Whilst working full time, I studied at Birkbeck, University of London, gaining an MSc Advanced Information Systems.

In 2010, during my MSc studies, I was introduced to machine learning and neural networks. Machine learning has since taken the world by storm and I've been happy to use this skillset in projects for my clients!

Technical Skills

I am a multilingual programmer and have previously worked in a number of languages including Java, PHP, and Perl. In recent years, full-stack JavaScript has been my preference, using Node.js on the backend and typically Vue.js or React on the frontend.

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