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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

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

Index

A

accuracy 246, 317

additional exploratory analysis, initial data exploration prompt 471-473

distribution of sentiment scores,visualizing 478

sentiment score calculation 475-477

text preprocessing 473

word frequency analysis 474, 475

advanced conversations 551-553

modeling advanced conversations 553, 554

pseudo code 554

agent programs 545

agents 545

powered by AI 546

working 546

AI assistance, adding to unsupervised learning model development process 386-389

checking for outliers 395-397

clustering model, creating 405, 406

clustering results, visualizing 406, 407

data, inspecting 390

data, preprocessing 392, 393

data scaling, with standardization 399, 400

dataset, loading 389

feature engineering 393-395

number of clusters, deciding 400- 405

outliers, removing 398, 399

summary statistics 391

AI assistance, in page structure 40

challenge 46

simple prompting 40-45

...

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Authors (5)

Christoffer Noring

Christoffer Noring

Christoffer Noring is a software developer with more than 10 years of experience. He has successfully delivered software for different industries, ranging from telecom to aviation. Throughout his career, he has worked on everything, right from databases to frontends. He is very passionate about community and sharing knowledge, which is why he frequently speaks on topics ranging from TDD, React, and NativeScript to Angular. He also writes books and blogs frequently. He holds the title of Google Developer Expert in web technologies and AngularJS/Angular. He is also a Telerik Developer Expert in the mobile framework NativeScript. Christoffer currently works for McKinsey as a fullstack developer. He is the author and maintainer of the book RxJS Ultimate, which aims to be a free resource to help the community.

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Ajit Jaokar

Ajit Jaokar

Ajit Jaokar is a data scientist for Feynlabs, building AI prototypes for complex applications. He is also a course director for AI at the University of Oxford. Besides this, Ajit is a visiting fellow in Engineering Sciences at the University of Oxford and conducts AI courses at the London School of Economics, Universidad Politecnica de Madrid, and the Harvard Kennedy School of Government as part of The Future Society. His work at Oxford and his company is based on interdisciplinary aspects of AI, including AI with digital twins, quantum computing, metaverse, Agtech, and life sciences. His teaching is based on a methodology for AI and cyber-physical systems, which he is developing as part of his research.

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Ayşe Mutlu

Ayşe Mutlu

Ayşe Mutlu is a data scientist working on Azure AI and DevOps technologies. Based in London, Ayşe's work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines). She enjoys coding in Python and contributing to open-source initiatives in Python.

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Marina Fernandez

Marina Fernandez

Marina Fernandez is a data science and Databricks consultant with expertise in financial risk management. She contributes to the academic team at the University of Oxford, where she holds the positions of senior AI and ML tutor and guest lecturer. Throughout her 20-year career, Marina has worked on the development of large-scale enterprise systems for various business domains. Her experience encompasses e-commerce, e-learning, software security, commodity trading, commodity trading and risk management systems, and regulatory reporting. Marina obtained her MSc in Software Engineering from the University of Oxford. Additionally, she has earned professional certifications, including Microsoft Certified Professional and Certified Scrum Master

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Anjali Jain

Anjali Jain

Anjali Jain is a London-based AI and ML professional with a career spanning over two decades. Currently working as a data architect for Metrobank, she brings her expertise in AI, data, architecture, data governance, and software development to the financial sector. Anjali holds a bachelor's degree in electrical engineering and boasts certifications, including TOGAF 9.1 and ITIL 2011 Foundation. In her role as Senior AI and ML tutor at Oxford, she shares cutting-edge knowledge on various technologies.

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