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  • Book Overview & Buying AI-Assisted Programming for Web and Machine Learning
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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

By : Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
4.9 (11)
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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

4.9 (11)
By: Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
Buy this Book

Overview of this book

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.
Table of Contents (25 chapters)
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
Free Chapter
1
It’s a New World, One with AI Assistants, and You’re Invited
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Introduction
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How ChatGPT came to be, from NLP to LLMs
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The new paradigm, programming with natural language
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About this book
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Who this book is for
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Evolution of programming languages
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How to use this book
2
Prompt Strategy
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Introduction
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Where you are
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Guidelines for how to prompt efficiently
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Prompt strategy for web development
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Prompt strategy for data science
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Validate the solution
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Summary
3
Tools of the Trade: Introducing Our AI Assistants
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Introduction
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Understanding Copilot
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Understanding ChatGPT
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Summary
4
Build the Appearance of Our App with HTML and Copilot
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Introduction
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Business problem: e-commerce
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Page structure
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Add AI assistance to our page structure
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Challenge: vary the prompt
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Use case: build a front for an e-commerce
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Assignment
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Challenge
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Quiz
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Summary
5
Style the App with CSS and Copilot
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Introduction
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Business problem: e-commerce
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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CSS, or Cascading Style Sheets
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Assignment
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Solution
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Use case: style the e-commerce app
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Challenge
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Quiz
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Summary
6
Add Behavior with JavaScript
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Introduction
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Business problem: e-commerce
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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Adding JavaScript
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Challenge
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Use case: adding behavior
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Assignment
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Solution
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Summary
7
Support Multiple Viewports Using Responsive Web Layouts
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Introduction
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Business problem: e-commerce
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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Viewports
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Use case: make our product gallery responsive
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Assignment
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Solution
8
Build a Backend with Web APIs
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Introduction
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Business domain: e-commerce
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Problem and data domain
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Feature breakdown
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Prompt strategy
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Web APIs
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Creating a Web API with Python and Flask
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Use case: a Web API for an e-commerce site
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Assignment
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Solution
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Challenge
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Summary
9
Augment Web Apps with AI Services
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Introduction
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Business domain, e-commerce
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Problem and data domain
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Feature breakdown
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Prompt strategy
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Creating a model
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Coming up with a plan
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Converting the model to ONNX
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Loading the ONNX model in JavaScript
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Assignment: Build a REST API in JavaScript that consumes the model
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Solution
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Quiz
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Summary
10
Maintaining Existing Codebases
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Introduction
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Prompt strategy
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Different types of maintenance
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The maintenance process
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Addressing a bug
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Adding a new feature
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Improving performance
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Improving maintainability
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Challenge
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Updating an existing e-commerce site
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Assignment
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Knowledge check
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Summary
11
Data Exploration with ChatGPT
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Introduction
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Business problem
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Problem and data domain
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Feature breakdown
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Prompting strategy
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Data exploration of the Amazon review dataset using the free version of ChatGPT
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Data exploration of the Amazon review dataset using ChatGPT-4o
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Assignment
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Challenge
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Summary
12
Building a Classification Model with ChatGPT
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Introduction
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Business problem
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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Building a sentiment analysis model to accurately classify Amazon reviews using the free version of ChatGPT
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Building a sentiment analysis model to accurately classify Amazon reviews using ChatGPT-4 or ChatGPT Plus
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Assignment
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Challenge
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Summary
13
Building a Regression Model for Customer Spend with ChatGPT
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Introduction
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Business problem
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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Building a simple linear regression model to predict the “Yearly Amount Spent” by customers using the free version of ChatGPT
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Learning simple linear regression using ChatGPT Plus
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Assignment
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Challenge
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Summary
14
Building an MLP Model for Fashion-MNIST with ChatGPT
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Introduction
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Business problem
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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Building an MLP model to accurately classify the Fashion-MNIST images using the free version of ChatGPT
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Assignment
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Challenge
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Summary
15
Building a CNN Model for CIFAR-10 with ChatGPT
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Introduction
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Business problem
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Problem and data domain
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Breaking the problem down into features
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Prompting strategy
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Building a CNN model to accurately classify the CIFAR-10 images using the free version of ChatGPT
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Assignment
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Challenge
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Summary
16
Unsupervised Learning: Clustering and PCA
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Introduction
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Breaking the problem down into features
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Prompt strategy
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Customer segmentation
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Product clustering for an e-commerce project
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Reflection on prompts for this use case
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Assignment
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Solution
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Summary
17
Machine Learning with Copilot
chevron up
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Introduction
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GitHub Copilot Chat in your IDE
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Dataset overview
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Steps for data exploration
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Prompt strategy
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Your initial data exploration prompt: Prompt 1, setting the high-level context
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Step 1: Load the dataset
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Step 2: Inspect the data
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Step 3: Summary statistics
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Step 4: Explore categorical variables
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Step 5: Distribution of ratings
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Step 6: Temporal analysis
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Step 7: Review length analysis
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Step 8: Correlation analysis
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Step 9: Additional exploratory analysis
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Step 10: Text Preprocessing
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Step 11: Word Frequency Analysis
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Step 12: Sentiment Score Calculation
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Step 13: Visualize the Distribution of Sentiment Scores
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Step 14: Analyze the Relationship Between Sentiment Score and Other Variables
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Assignment
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Solution
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Summary
18
Regression with Copilot Chat
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Introduction
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Regression
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Dataset overview
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Prompt strategy
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Evaluate the model
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Assignment
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Summary
19
Regression with Copilot Suggestions
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Introduction
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Dataset overview
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Prompt strategy
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Start coding with Copilot’s help
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Assignment
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Summary
20
Increasing Efficiency with GitHub Copilot
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Introduction
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Code generation and automation
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Copilot commands
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Debugging and troubleshooting
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Code review and optimization techniques
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Workspace
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Visual Studio Code lookup
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Terminal
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Assignment
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Challenge
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Quiz
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Summary
21
Agents in Software Development
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Introduction
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What are agents?
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Simpler agents versus agents using AI
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Simpler agents
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Advanced conversations
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Autonomous agents
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Assignment
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Challenge
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Quiz
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Summary
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References
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Conclusion
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Conclusion
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Recap of the book
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Major conclusions
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What’s next
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At last
23
Other Books You May Enjoy
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Other Books You May Enjoy
24
Index
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Index

Step 12: Sentiment Score Calculation

You can calculate a sentiment score for each review using a sentiment analysis tool like TextBlob or Vader. This can provide a more quantitative measure of the sentiment expressed in the reviews.

from textblob import TextBlob
# Define a function to calculate sentiment
def calculate_sentiment(text):
    return TextBlob(text).sentiment.polarity
# Apply the function to the review_body_clean column
df['sentiment_score'] = df['review_body_clean'].apply(calculate_sentiment)
# Print the average sentiment score
print(df['sentiment_score'].mean())

Please note that you’ll need to install the nltk and textblob libraries to run this code, which you can do using pip:

pip install nltk textblob

Let me know if you’d like to proceed with any of these steps, or if there’s something else you’d like to do.

[End of response]

This prompt yielded three steps as a response. Let’...

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