Member-only story

The Ultimate Guide to Building Machine Learning Applications Integrated with Web Technology

Gabriel Mistral

·10.6k Followers· Follow

Published in Hands On Machine Learning With TensorFlow Js: A Guide To Building ML Applications Integrated With Web Technology Using The TensorFlow Js Library

4 min read · 3 weeks before

1.3k View Claps

76 Respond

Save

Listen

Machine Learning (ML) is a rapidly growing field that has seen significant advancements in recent years. With its ability to automate tasks and make predictions, ML is transforming industries around the world. As a result, there is a growing demand for developers who can build and deploy ML applications.

This guide will teach you everything you need to know to build ML applications integrated with web technology. We'll cover the basics of ML, web development, and how to integrate the two together. We'll also provide hands-on examples that you can use to get started building your own ML applications.

This chapter will provide an overview of ML, including its history, different types of ML algorithms, and how ML is used in practice. By the end of this chapter, you will have a solid understanding of the basics of ML and how it can be used to solve real-world problems.

Hands-On Machine Learning with TensorFlow.js: A guide to building ML applications integrated with web technology using the TensorFlow.js library

by Kai Sasaki

4.7 out of 5

Language	:	English
File size	:	35781 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	296 pages

This chapter will provide an overview of web development, including the different technologies used to build web applications. By the end of this chapter, you will have a basic understanding of how web applications are built and how ML can be integrated into them.

This chapter will cover how to integrate ML into web applications. We'll discuss different approaches to integrating ML, including using ML libraries and frameworks, and we'll provide hands-on examples of how to build ML-powered web applications.

This chapter will cover how to deploy ML applications. We'll discuss different deployment options, including cloud-based deployment and on-premises deployment, and we'll provide hands-on examples of how to deploy ML applications to different platforms.

This chapter will provide case studies of real-world ML applications integrated with web technology. We'll discuss the challenges and lessons learned from building and deploying these applications, and we'll provide insights into how you can use ML to build your own successful applications.

This guide has provided you with a comprehensive overview of how to build ML applications integrated with web technology. We've covered everything from the basics of ML and web development to how to integrate the two together and deploy ML applications. We hope this guide has helped you gain the knowledge and skills you need to build your own successful ML applications.

Alt attributes for images: