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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

Regression

Let’s start by asking Copilot Chat for the definition of regression:

[Prompt]

What is regression?

[End of prompt]

You should receive a response similar to the response below:

[Prompt response]

Regression is a statistical method used in data analysis which investigates the relationship between a dependent (target) and independent variable(s) (predictor). This method is mostly used for forecasting, time series modeling, and finding the causal effect relationship between the variables. For example, the relationship between rainfall and soil erosion or the relationship between salary and years of experience.

[End of response]

You may notice in your chatbox that GitHub Copilot is suggesting the next context, highlighted in blue, asking, “What are the different types of regression?”. You can click on this to get the answer. This demonstrates how Copilot is a powerful tool that assists users effectively.

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