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

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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 section, we aim to build a classification model for customer review sentiment analysis using the Amazon product review dataset. Leveraging ChatGPT’s capabilities, we’ll generate Python code to construct a classification model, offering readers a practical approach to working with datasets and understanding classification techniques. Additionally, we’ll explore effective prompting techniques to guide ChatGPT in providing tailored code snippets and insights for data classification tasks.

Dataset overview

The Amazon product review dataset contains information on various products and their corresponding reviews. By utilizing this dataset, we can perform various analyses, including sentiment analysis, trend analysis of customer feedback, and product rating analysis. The ultimate goal is to train a classification model capable of accurately classifying reviews into positive or negative sentiments, enhancing decision-making processes...