AI-assisted Programming for Web and Machine Learning

Artificial intelligence is redefining how software is created, enabling developers to code faster, improve accuracy, and bring innovative ideas to life. In today’s competitive technology landscape, AI-assisted programming is no longer optional; it’s a core skill for building modern web applications and machine learning solutions.

This book systematically guides you through the whole development cycle. You will start with the foundations of understanding AI-assisted programming, setting up your environment, and mastering prompt engineering. You will then discover how AI can accelerate front end and back end web development, enhance debugging, and optimize performance. You will also explore data preprocessing, model creation, training, and deploying optimized solutions with the help of real-world examples, case studies, and hands-on exercises, ensuring you can apply every concept in practice. Also, by mastering prompt engineering, you will be able to generate, debug, and optimize code across both these high-demand fields.

By the end of this book, you will have the confidence and skills to integrate AI into your workflow, automate time-consuming tasks, build intelligent applications, and deliver impactful, future-ready solutions.

What you will learn

● Apply prompt engineering effectively for web and ML projects.

● Develop AI-powered front end and back end applications efficiently.

● Automate debugging, testing, and performance optimization with AI.

● Integrate AI tools seamlessly into full-stack development workflows.

● Train, fine-tune, and deploy scalable ML models in the cloud.

● Understand AI-assisted programming concepts and set up development tools.

● Preprocess data and create AI-driven machine learning pipelines.

Who this book is for

This book is for learners who want to explore the power of AI-assisted programming in web development and machine learning. Software engineers, web developers, and data scientists who possess foundational programming skills, ideally with Python or JavaScript, can also use it to upgrade their skills.

Table of Contents

1. AI in Programming

2. Setting up Your AI Environment

3. Prompt Engineering

4. AI in Front end Development

5. AI for Back end Development

6. Debugging and Optimization with AI

7. Data Preprocessing with AI

8. Building and Training Machine Learning Models

9. Deploying Optimized ML Models

10. Real-world Applications

11. Future Innovations and Ethics in AI