Speed up your development processes and improve your productivity by writing practical and relevant prompts to build web applications and Machine Learning (ML) models

Key Features

• Utilize prompts to enhance frontend and backend web development
• Develop prompt strategies to build robust machine learning models
• Use GitHub Copilot for data exploration, maintaining existing code bases, and augmenting ML models into web applications

Book Description

AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks.

Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code.

Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases.

The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.

What you will learn

• Speed up your coding and machine learning workflows with GitHub Copilot and ChatGPT
• Use an AI-assisted approach across the development lifecycle
• Implement prompt engineering techniques in the data science lifecycle
• Develop the frontend and backend of a web application with AI assistance
• Build machine learning models with GitHub Copilot and ChatGPT
• Refactor code and fix faults for better efficiency and readability
• Improve your codebase with rich documentation and enhanced workflows

Who this book is for

Experienced developers new to GitHub Copilot and ChatGPT can discover the best strategies to improve productivity and deliver projects quicker than traditional methods. This book is ideal for software engineers working on web or machine learning projects. It is also a useful resource for web developers, data scientists, and analysts who want to improve their efficiency with the help of prompting. This book does not teach web development or how different machine learning models work.

Table of Contents

Chapter 1: It's a New World, One with Al Assistants, and You're Invited

Chapter 2: Prompt Strategy

Chapter 3: Tools of the Trade: Introducing Our Al Assistants

Chapter 4: Build the Appearance of Our App with HTML and Copilot

Chapter 5: Style the App with CSS and Copilot

Chapter 6: Add Behavior with JavaScript

Chapter 7: Support Multiple Viewports Using Responsive Web Layouts

Chapter 8: Build a Backend with Web APls

Chapter 9: Augment Web Apps with Al Services

Chapter 10: Maintaining Existing Code bases

Chapter 11: Data Exploration with ChatGPT

Chapter 12: Building a Classification Model with ChatGPT

Chapter 13: Building a Regression Model for Customer Spend with ChatGPT

Chapter 14: Building an MLP Model for Fashion-MNIST with ChatGPT

Chapter 15: Building a CNN Model for CIFAR-10 with ChatGPT

Chapter 16: Unsupervised Learning: Clustering and PCA

Chapter 17: Machine Learning with Copilot

Chapter 18: Regression with Copilot Chat

Chapter 19: Regression with Copilot Suggestions

Chapter 20: Increasing Efficiency with GitHub Copilot

Chapter 21: Agents in Software Development

Chapter 22: Conclusion

About the Authors

Christoffer Noring works as a Senior Advocate at Microsoft and focuses on application development and AI. He's a Google Developer Expert and a public speaker on 100+ presentations across the world. Additionally, he's a tutor at the University of Oxford on cloud patterns and AI. Chris is also a published author on Angular, NGRX, and programming with Go.

Anjali Jain is a London-based AI and ML professional with a career spanning over two decades. Currently working as a data architect for Metrobank, she brings her expertise in AI, data, architecture, data governance, and software development to the financial sector. Anjali holds a bachelor's degree in electrical engineering and boasts certifications, including TOGAF 9.1 and ITIL 2011 Foundation. In her role as Senior AI and ML tutor at Oxford, she shares cutting-edge knowledge on various technologies.

Marina Fernandez is a data science and Databricks consultant with expertise in financial risk management. She contributes to the academic team at the University of Oxford, where she holds the positions of senior AI and ML tutor and guest lecturer. Throughout her 20-year career, Marina has worked on the development of large-scale enterprise systems for various business domains. Her experience encompasses e-commerce, e-learning, software security, commodity trading, commodity trading and risk management systems, and regulatory reporting. Marina obtained her MSc in Software Engineering from the University of Oxford. Additionally, she has earned professional certifications, including Microsoft Certified Professional and Certified Scrum Master

Ayşe Mutlu is a data scientist working on Azure AI and DevOps technologies. Based in London, Ayşe's work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines). She enjoys coding in Python and contributing to open-source initiatives in Python.

Ajit Jaokar is a data scientist for Feynlabs, building AI prototypes for complex applications. He is also a course director for AI at the University of Oxford. Besides this, Ajit is a visiting fellow in Engineering Sciences at the University of Oxford and conducts AI courses at the London School of Economics, Universidad Politecnica de Madrid, and the Harvard Kennedy School of Government as part of The Future Society. His work at Oxford and his company is based on interdisciplinary aspects of AI, including AI with digital twins, quantum computing, metaverse, Agtech, and life sciences. His teaching is based on a methodology for AI and cyber-physical systems, which he is developing as part of his research.