Get practical advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, design, coding, debugging, testing, and documentation. With this book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs (ChatGPT, Gemini, and Claude) to code-specific systems (GitHub Copilot, Tabnine, Cursor, and Amazon CodeWhisperer). You'll also learn about more specialized generative AI tools for tasks such as text-to-image creation. Author Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code. This guide also describes the best ways of using general purpose LLMs to learn a programming language, explain code, or convert code from one language to another. This book examines: The core capabilities of AI-based development tools Pros, cons, and use cases of popular systems such as GitHub Copilot and Amazon CodeWhisperer Ways to use ChatGPT, Gemini, Claude, and other generic LLMs for coding Using AI development tools for the software development lifecycle, including requirements, planning, coding, debugging, and testing Prompt engineering for development Using AI-assisted programming for tedious tasks like creating regular expressions, starter code, object-oriented programming classes, and GitHub Actions How to use AI-based low-code and no-code tools, such as to create professional UIs

Speed up your development processes and improve your productivity by writing practical and relevant prompts to build web applications and Machine Learning (ML) models Purchase of the print or Kindle book includes a free PDF copy 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 DescriptionAI-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.

Writing computer programs in Python just got a lot easier! Use AI-assisted coding tools like GitHub Copilot and ChatGPT to turn your ideas into applications faster than ever. AI has changed the way we write computer programs. With tools like Copilot and ChatGPT, you can describe what you want in plain English, and watch your AI assistant generate the code right before your eyes. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT you’ll learn how to: Write fun and useful Python applications—no programming experience required! Use the Copilot AI coding assistant to create Python programs Write prompts that tell Copilot exactly what to do Read Python code and understand what it does Test your programs to make sure they work the way you want them to Fix code with prompt engineering or human tweaks Apply Python creatively to help out on the job Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT is a hands-on beginner’s guide that is written by two esteemed computer science university professors. It teaches you everything you need to start programming Python in an AI-first world. You’ll hit the ground running, writing prompts that tell your AI-assistant exactly what you want your programs to do. Along the way, you’ll pick up the essentials of Python programming and practice the higher-level thinking you’ll need to create working apps for data analysis, automating tedious tasks, and even video games. Foreword by Beth Simon, Ph.D. About the technology The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. About the book This book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates. What's inside Prompts for working code Tweak code manually and with AI help AI-test your programs Let AI handle tedious details About the reader If you can move files around on your computer and install new programs, you can learn to write useful software! About the author Dr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan. Table of Contents 1 Introducing AI-assisted programming with Copilot 2 Getting started with Copilot 3 Designing functions 4 Reading Python code – Part 1 5 Reading Python Code – Part 2 6 Testing and prompt engineering 7 Problem decomposition 8 Debugging and better understanding your code 9 Automating tedious tasks 10 Making some games 11 Future directions

See how an AI assistant can bring your ideas to life immediately! Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way—with the help of AI. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming, Second Edition you’ll learn how to: • Write fun and useful Python applications—no programming experience required! • Use the GitHub Copilot AI coding assistant to create Python programs • Write prompts that tell Copilot exactly what to do • Read Python code and understand what it does • Test your programs to make sure they work the way you want them to • Fix code with prompt engineering or human tweaks • Apply Python creatively to help out on the job AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it teaches you everything you need to start programming Python in an AI-first world. You’ll learn skills you can use to create working apps for data analysis, automating tedious tasks, and even video games. Plus, in this new edition, you’ll find groundbreaking techniques for breaking down big software projects into smaller tasks AI can easily achieve. Foreword by Beth Simon. About the technology The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. About the book This book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates. What's inside • Prompts for working code • Tweak code manually and with AI help • AI-test your programs • Let AI handle tedious details About the reader If you can move files around on your computer and install new programs, you can learn to write useful software! About the author Dr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan. Table of Contents 1 Introducing AI-assisted programming with GitHub Copilot 2 Getting started with Copilot 3 Designing functions 4 Reading Python code: Part 1 5 Reading Python code: Part 2 6 Testing and prompt engineering 7 Problem decomposition 8 Debugging and better understanding your code 9 Automating tedious tasks 10 Making some games 11 Creating an authorship identification program 12 Future directions

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

"Coding with AI: A Beginner's Guide to AI-Assisted Programming" is your essential roadmap to navigating the exciting world of AI-powered software development. From mastering productivity-boosting techniques to understanding the ethical implications of AI in coding, this comprehensive guide equips you with the knowledge and skills needed to thrive in the rapidly evolving landscape of AI-assisted programming. Whether you're a seasoned developer or just starting your coding journey, this book will help you harness the power of AI to enhance your programming capabilities and prepare for the future of software development. AI-assisted programming, machine learning in coding, software development automation, AI coding tools, natural language programming, ethical AI coding, future of programming, AI code generation, developer productivity, AI in software engineering

Get practical advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, design, coding, debugging, testing, and documentation. With this book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs (ChatGPT, Gemini, and Claude) to code-specific systems (GitHub Copilot, Tabnine, Cursor, and Amazon CodeWhisperer). You'll also learn about more specialized generative AI tools for tasks such as text-to-image creation. Author Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code. This guide also describes the best ways of using general purpose LLMs to learn a programming language, explain code, or convert code from one language to another. This book examines: The core capabilities of AI-based development tools Pros, cons, and use cases of popular systems such as GitHub Copilot and Amazon CodeWhisperer Ways to use ChatGPT, Gemini, Claude, and other generic LLMs for coding Using AI development tools for the software development lifecycle, including requirements, planning, coding, debugging, and testing Prompt engineering for development Using AI-assisted programming for tedious tasks like creating regular expressions, starter code, object-oriented programming classes, and GitHub Actions How to use AI-based low-code and no-code tools, such as to create professional UIs