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

Dataset overview

This dataset provides us with a wealth of information about Apple’s stock (traded under AAPL) over the past decade, starting from the year 2010. This data is incredibly valuable because it can help us develop forecasting algorithms to predict the future price of Apple’s stock, which is crucial for making investment decisions. The data in this set has been collected and aggregated from 25 different stock exchanges.

To effectively use this data for forecasting, we need to understand the key elements: the features that influence our target, which is predicting stock prices.

The dataset includes five important values that indicate how the stock price changes over a specific period of time, which is typically one day, but it could also be one week or one month. These values are:

Open: This is the stock price at the beginning of the trading day.
Close: This is the stock price at the end of the trading day.
High: This value...