Machine Learning and TypeScript: From Idea to Production

The book "Machine Learning and TypeScript" is a unique practical guide that bridges the gap between modern web development and the world of artificial intelligence. Unlike most ML books focused on Python, this edition is built entirely on TypeScript — a language that dominates frontend and backend development today.

What this book is about

The book will take you through the complete cycle of creating ML applications: from mathematical foundations to deploying ready-made models in production. You will learn how to use the TensorFlow.js library to build neural networks, process texts using NLP, and work with images.

Who this book is for

• Web developers who want to add machine learning to their tech stack
• TypeScript developers looking to transition into Data Science without learning Python
• Students and aspiring ML engineers seeking a practical approach to learning
• Team Leads and architects planning to integrate AI features into web projects

What you will learn

• Fundamentals of machine learning: linear regression, decision trees, clustering
• Building neural networks with TensorFlow.js and Keras API
• Natural Language Processing (NLP): tokenization, text classification, generation
• Computer vision: object recognition, image classification
• Optimizing models for the browser and Node.js
• Deploying ML models to production (Docker, AWS, Vercel)
• Integration with popular frameworks: React, Next.js, NestJS

Book features

• All code examples in TypeScript with strict typing
• Step-by-step projects: from handwritten digit recognition to a chatbot
• Real-world web development use cases
• Performance and optimization tips

This book is not just theoretical material. It is a tool that will allow you to create modern, intelligent applications using the familiar TypeScript language. Whether you want to add a recommendation system to an online store or build a neural network for content generation — you will find all the necessary knowledge here.

Get ready for intensive work with code, real datasets, and production solutions. After reading, you will be able to confidently create ML products without leaving the TypeScript ecosystem.