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AI-Assisted Programming for Web and Machine Learning

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835086056
Length 602 pages
Edition 1st Edition
Languages
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Authors (5):
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Marina Fernandez Marina Fernandez
Author Profile Icon Marina Fernandez
Marina Fernandez
Ajit Jaokar Ajit Jaokar
Author Profile Icon Ajit Jaokar
Ajit Jaokar
Anjali Jain Anjali Jain
Author Profile Icon Anjali Jain
Anjali Jain
Christoffer Noring Christoffer Noring
Author Profile Icon Christoffer Noring
Christoffer Noring
Ayşe Mutlu Ayşe Mutlu
Author Profile Icon Ayşe Mutlu
Ayşe Mutlu
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Toc

Table of Contents (25) Chapters Close

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

Step 4: Explore categorical variables

Just like before, let’s construct a prompt consisting of the output of the code we just ran and let Copilot tell us what should happen next:

[Prompt]

Below is the output:

Unnamed: 0 star_rating helpful_votes total_votes review_year count 100.000000 100.000000 100.000000 100.000000 100.0 mean 49.500000 0.840000 0.540000
0.960000 2015.0 std 29.011492 0.368453 1.298173 2.420118 0.0 min 0.000000 0.000000 0.000000 0.000000 2015.0 25% 24.750000 1.000000 0.000000 0.000000 2015.0 50% 49.500000 1.000000 0.000000 0.000000 2015.0 75% 74.250000 1.000000 1.000000 1.000000 2015.0 max 99.000000 1.000000 9.000000 14.000000 2015.0

[End of prompt]

You should receive a response similar to the response below:

[Prompt response]

Great! Now that we have the summary statistics, let’s move on to step 4: exploring categorical variables. We’ll analyze the unique values and their frequencies for categorical variables like...

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