Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying AI-Assisted Programming for Web and Machine Learning
  • Table Of Contents Toc
  • Feedback & Rating feedback
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)
close
close
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)
close
close
3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

Evaluate the model

As suggested by the response, Copilot recommends asking the following: What are some common metrics for evaluating regression models? Let’s select this and see what it says.

Evaluation metrics

Let’s ask Copilot about evaluation metrics.

[Prompt]

What are some common metrics for evaluating regression models?

[End of prompt]

You should receive a response similar to the response below:

[Prompt response]

There are several common metrics for evaluating the performance of regression models:

  • Mean Absolute Error (MAE): This is the average of the absolute differences between the predicted and actual values. It gives an idea of how wrong the predictions were.
  • Mean Squared Error (MSE): This is the average of the squared differences between the predicted and actual values. Squaring the differences amplifies the impact of large errors.
  • Root Mean Squared Error (RMSE): This is the square root of the MSE...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY