Building Machine Learning Solutions with TensorFlow.js

In this course, you'll learn to use TensorFlow.js to build, train, and deploy machine learning and deep learning models to power client-side and server-side applications using the JavaScript language.
Course info
Level
Intermediate
Updated
Dec 2, 2020
Duration
4h 7m
Table of contents
Course Overview
Introduction
Setting up TensorFlow.js Environment
Understanding TensorFlow.js Core Concepts
Preparing Data for Machine Learning Model: Part 1
Preparing Data for Machine Learning Model: Part 2
Building, Training, and Evaluating Machine Learning Model
Saving and Loading Machine Learning Model
Predicting Using Trained Machine Learning Model
Using Pre-trained Models with TensorFlow.js
What's Next?
Description
Course info
Level
Intermediate
Updated
Dec 2, 2020
Duration
4h 7m
Description

Machine learning and deep learning are powering some of the most groundbreaking applications of the current era. However, up until recently, JavaScript was not considered the go-to language for machine learning model development and deployment, despite being one of the most popular languages in the world. TensorFlow.js now allows JavaScript developers to extend their skills to build, train, and deploy machine learning and deep learning models. In this course, Building Machine Learning Solutions with TensorFlow.js, you'll learn about the TensorFlow.js ecosystem and how to set it up on the client-side in the browser and on the server-side with Node.js. First, you'll discover how to use the environment to build an end-to-end machine learning application that uses natural language processing (NLP) under the hood to detect toxic elements in unstructured text. Next, you'll learn how to import and process data, build, train, and export a model, and finally predict using the trained model. Finally, you'll explore how to use existing models trained in Python on the client-side using TensorFlow.js, and even retrain the pre-trained model using transfer learning. By the end of this course, you'll have the skills and knowledge of TensorFlow.js to build, train, and deploy machine learning and deep learning models on the client-side, as well as on the server-side that can power sophisticated applications.

About the author
About the author

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, everyone. My name is Abhishek Kumar, and welcome to my course on Building Machine Learning Solutions with TensorFlow.js. I'm a data science consultant, Google Developers Expert for machine learning, and also graduate from UC Berkeley. Machine learning and deep learning are powering some of the most groundbreaking applications of the current era. However, up until recently, JavaScript was not considered as the go‑to language for machine learning model development and deployment, despite being one of the most popular languages in the world. TensorFlow.js open source framework now allows JavaScript developers to extend their JavaScript skills to build machine learning powered applications that can solve the key challenges of data privacy, network latency, application availability, and compute cost while keeping performance in check and providing the flexibility to run TensorFlow.js on client browsers, mobile native applications, IoT edge devices, as well as on the servers. In this course, we are going to use TensorFlow.js to build and train models from scratch, use existing trained models, retrain pretrained models using transfer learning, and run TensorFlow.js on the client side, as well as on the server side. By the end of this course, you will know how to use TensorFlow.js' ecosystem to build, train, and deploy machine learning models. Before beginning this course, you should be familiar with the basics of JavaScript and machine learning and deep learning. I hope you will join me on this journey to learn TensorFlow.js with the Building Machine Learning Solutions with TensorFlow.js course, at Pluralsight.