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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

By : Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
4.9 (11)
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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

4.9 (11)
By: Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

Overview of this book

AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks. Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.
Table of Contents (25 chapters)
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3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

Introduction

Building on the foundation set in the previous chapter, where we used ChatGPT for data exploration with Amazon book reviews, Chapter 12 delves deeper into the realm of supervised learning, with a focus on classification. Here, we continue to leverage ChatGPT, applying its capabilities to enhance our understanding and application of supervised learning techniques in the context of customer reviews.

In the realm of e-commerce, customer feedback plays a pivotal role in shaping business strategies and product enhancements. As Bill Gates aptly stated, “Your most dissatisfied customers are your greatest source of learning.” Customer sentiments are often buried within the extensive pool of product reviews. However, manually scrutinizing this ocean of reviews, which includes various attributes such as product ID, title, text, rating, and helpful votes, is an arduous and often unmanageable task.

In this chapter, we concentrate on classifying customer reviews...

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