AI-Assisted Programming for Web and Machine Learning Improve Your Development Workflow with ChatGPT and GitHub Copilot

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Published
Birmingham : Packt Publishing, Limited, 2024.
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Available Online

Description

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 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.

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Format
Language
English
ISBN
9781835083895, 1835083897

Notes

General Note
Description based upon print version of record.
General Note
Business problem: e-commerce
Description
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 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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

Table of Contents

Cover
Copyright
Contributors
Preface
Chapter 1: It's a New World, One with AI Assistants, and You're Invited
Introduction
How ChatGPT came to be, from NLP to LLMs
The rise of LLMs
GPT models
How LLMs are better
The new paradigm, programming with natural language
Challenges and limitations
About this book
Who this book is for
Evolution of programming languages
Looking ahead
How to use this book
Chapter 2: Prompt Strategy
Introduction
Where you are
Guidelines for how to prompt efficiently
Prompt techniques
Task-Action-Guideline prompt pattern (TAG)
Persona-Instruction-Context prompt pattern (PIC)
Exploratory prompt pattern
Learn-Improvise-Feedback-Evaluate prompt pattern (LIFE)
Which pattern to choose?
Prompt strategy for web development
Break down the problem: "web system for inventory management"
Further breakdown of the frontend into features
Generate prompts for each feature
Identify some basic principles for web development, a "prompt strategy"
Prompt strategy for data science
Problem breakdown: predict sales
Further breakdown into features/steps for data science
Generate prompts for each step
Identify some basic principles for data science, "a prompt strategy for data science"
Validate the solution
Verification via prompts
Classical verification
Summary
Chapter 3: Tools of the Trade: Introducing Our AI Assistants
Introduction
Understanding Copilot
How Copilot knows what to generate
Copilot capabilities and limits
Setup and installation
Installing Copilot
Getting started with Copilot
Assignment: improve the code
Solution
Challenge
References
Understanding ChatGPT
How does ChatGPT work?
ChatGPT capabilities and limits
Setup and installation
Getting started with ChatGPT
Prompting
Summary
Chapter 4: Build the Appearance of Our App with HTML and Copilot
Introduction
Business problem: e-commerce
Problem domain
Problem breakdown: identify the features
Prompt strategy
Page structure
Add AI assistance to our page structure
Your first prompt, simple prompting, and aiding your AI assistant
Your second prompt: adding more context
Your third prompt: accept prompt suggestions
Challenge: vary the prompt
Use case: build a front for an e-commerce
Login page
Product list page
Remaining pages
Assignment
Challenge
Quiz
Summary
Chapter 5: Style the App with CSS and Copilot
Introduction
Business problem: e-commerce
Problem and data domain
Breaking the problem down into features
Prompting strategy
CSS, or Cascading Style Sheets
First CSS
CSS by name
Assignment
Solution
Use case: style the e-commerce app
Basket page
Challenge
Quiz
Summary
Chapter 6: Add Behavior with JavaScript
Introduction

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Citations

APA Citation, 7th Edition (style guide)

Noring, C., Jain, A., Fernandez, M., Mutlu, A., & Jaokar, A. (2024). AI-Assisted Programming for Web and Machine Learning: Improve Your Development Workflow with ChatGPT and GitHub Copilot . Packt Publishing, Limited.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Christoffer, Noring et al.. 2024. AI-Assisted Programming for Web and Machine Learning: Improve Your Development Workflow With ChatGPT and GitHub Copilot. Birmingham: Packt Publishing, Limited.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Christoffer, Noring et al.. AI-Assisted Programming for Web and Machine Learning: Improve Your Development Workflow With ChatGPT and GitHub Copilot Birmingham: Packt Publishing, Limited, 2024.

Harvard Citation (style guide)

Noring, C., Jain, A., Fernandez, M., Mutlu, A. and Jaokar, A. (2024). AI-assisted programming for web and machine learning: improve your development workflow with chatgpt and github copilot. Birmingham: Packt Publishing, Limited.

MLA Citation, 9th Edition (style guide)

Noring, Christoffer,, et al. AI-Assisted Programming for Web and Machine Learning: Improve Your Development Workflow With ChatGPT and GitHub Copilot Packt Publishing, Limited, 2024.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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0ba1915e-abd5-50ce-4833-a0575f1a1369-eng
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Grouped Work ID0ba1915e-abd5-50ce-4833-a0575f1a1369-eng
Full titleai assisted programming for web and machine learning improve your development workflow with chatgpt and github copilot
Authornoring christoffer
Grouping Categorybook
Last Update2025-03-20 03:14:21AM
Last Indexed2025-05-22 03:02:34AM

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5050 |a Cover -- Copyright -- Contributors -- Preface -- Chapter 1: It's a New World, One with AI Assistants, and You're Invited -- Introduction -- How ChatGPT came to be, from NLP to LLMs -- The rise of LLMs -- GPT models -- How LLMs are better -- The new paradigm, programming with natural language -- Challenges and limitations -- About this book -- Who this book is for -- Evolution of programming languages -- Looking ahead -- How to use this book -- Chapter 2: Prompt Strategy -- Introduction -- Where you are -- Guidelines for how to prompt efficiently -- Prompt techniques
5058 |a Task-Action-Guideline prompt pattern (TAG) -- Persona-Instruction-Context prompt pattern (PIC) -- Exploratory prompt pattern -- Learn-Improvise-Feedback-Evaluate prompt pattern (LIFE) -- Which pattern to choose? -- Prompt strategy for web development -- Break down the problem: "web system for inventory management" -- Further breakdown of the frontend into features -- Generate prompts for each feature -- Identify some basic principles for web development, a "prompt strategy" -- Prompt strategy for data science -- Problem breakdown: predict sales
5058 |a Further breakdown into features/steps for data science -- Generate prompts for each step -- Identify some basic principles for data science, "a prompt strategy for data science" -- Validate the solution -- Verification via prompts -- Classical verification -- Summary -- Chapter 3: Tools of the Trade: Introducing Our AI Assistants -- Introduction -- Understanding Copilot -- How Copilot knows what to generate -- Copilot capabilities and limits -- Setup and installation -- Installing Copilot -- Getting started with Copilot -- Assignment: improve the code -- Solution -- Challenge -- References
5058 |a Understanding ChatGPT -- How does ChatGPT work? -- ChatGPT capabilities and limits -- Setup and installation -- Getting started with ChatGPT -- Prompting -- Summary -- Chapter 4: Build the Appearance of Our App with HTML and Copilot -- Introduction -- Business problem: e-commerce -- Problem domain -- Problem breakdown: identify the features -- Prompt strategy -- Page structure -- Add AI assistance to our page structure -- Your first prompt, simple prompting, and aiding your AI assistant -- Your second prompt: adding more context -- Your third prompt: accept prompt suggestions
5058 |a Challenge: vary the prompt -- Use case: build a front for an e-commerce -- Login page -- Product list page -- Remaining pages -- Assignment -- Challenge -- Quiz -- Summary -- Chapter 5: Style the App with CSS and Copilot -- Introduction -- Business problem: e-commerce -- Problem and data domain -- Breaking the problem down into features -- Prompting strategy -- CSS, or Cascading Style Sheets -- First CSS -- CSS by name -- Assignment -- Solution -- Use case: style the e-commerce app -- Basket page -- Challenge -- Quiz -- Summary -- Chapter 6: Add Behavior with JavaScript -- Introduction
520 |a 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 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.
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