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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

Building a sentiment analysis model to accurately classify Amazon reviews using ChatGPT-4 or ChatGPT Plus

ChatGPT Plus or GPT-4 includes the ability to upload a dataset, analyze the dataset, and produce results if using simple libraries such as Python and matplotlib. At the time of writing this chapter, it had an AI assistant named Data Analysis, provided by OpenAI when using the mobile app, or when selecting GPT-4 if using the browser version.

Let’s explore how it differs from the free version, feature by feature.

Feature 1: Data preprocessing and feature engineering

Let’s craft our initial prompt for the baseline model:

[Prompt]

I want to create a simple classification model for sentiment analysis of the Amazon Review Dataset (TAG 1.1). <upload dataset in CSV format here­­­­­> (PIC 2.3)

It should consist of the following steps (TAG 1.2) and I am a beginner user (PIC 2.1), so provide one step at a time and wait...

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