You're reading from AI-Assisted Programming for Web and Machine Learning Improve your development workflow with ChatGPT and GitHub Copilot

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835086056

Length 602 pages

Edition 1st Edition

Languages

Python

Tools

ChatGPT

Concepts

Artificial Intelligence

Authors (5):

Marina Fernandez

Ajit Jaokar

Anjali Jain

Christoffer Noring

Ayşe Mutlu

+1 more

View More author details

Table of Contents (25) Chapters

Preface

1. It’s a New World, One with AI Assistants, and You’re Invited FREE CHAPTER

2. Prompt Strategy

3. Tools of the Trade: Introducing Our AI Assistants

4. Build the Appearance of Our App with HTML and Copilot

5. Style the App with CSS and Copilot

6. Add Behavior with JavaScript

7. Support Multiple Viewports Using Responsive Web Layouts

8. Build a Backend with Web APIs

9. Augment Web Apps with AI Services

10. Maintaining Existing Codebases

11. Data Exploration with ChatGPT

12. Building a Classification Model with ChatGPT

13. Building a Regression Model for Customer Spend with ChatGPT

14. Building an MLP Model for Fashion-MNIST with ChatGPT

15. Building a CNN Model for CIFAR-10 with ChatGPT

16. Unsupervised Learning: Clustering and PCA

17. Machine Learning with Copilot

18. Regression with Copilot Chat

19. Regression with Copilot Suggestions

20. Increasing Efficiency with GitHub Copilot

21. Agents in Software Development

22. Conclusion

23. Other Books You May Enjoy

24. Index

Problem and data domain

In this chapter, we will focus exclusively on detailed data exploration using the Amazon product review dataset. Our goal is to deeply explore this dataset to unearth insights and discern patterns that can enhance decision-making. We’ll leverage ChatGPT to generate Python code for data manipulation and visualization, providing a hands-on approach to understanding complex data analysis techniques. Additionally, we will explore methods to effectively prompt ChatGPT to deliver tailored insights and code snippets that aid in our exploration tasks.

Dataset overview

We will work with the Amazon product review dataset, which includes a broad range of information reflecting consumer feedback and product evaluations. Key features of this dataset encompass identifiers such as marketplace, customer, review, and product details, as well as product titles, categories, ratings, and the textual content of reviews. For this exploration, we’ll concentrate...