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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

Dataset overview

This dataset contains Apple’s (AAPL) stock data spanning 10 years. Insights from this data can be used to build useful price forecasting algorithms to aid investment.

We can make the following notes about this dataset:

Data is collected and aggregated from 25 exchanges.
Data is updated weekly.
This dataset contains 1-minute, 5-minute, 30-minute, and 1-hour bars (open/high/low/close/volume) for AAPL.
Out-of-hours trades are included. Prices are adjusted for splits and dividends. Dataset available at https://www.kaggle.com/datasets/tarunpaparaju/apple-aapl-historical-stock-data.

Explore the dataset

To better illustrate this regression approach, it is necessary to have suitable predictors for our target. The dataset recorded more direct elements to the stock. Five values illustrating movements in the price over one unit of time (usually one day, but it can also be one week or one month) are key trading indicators...

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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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