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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

Customer segmentation

Clustering can help segment customers based on their purchasing behavior, preferences, or demographic information. By analyzing customer data such as browsing history, purchase history, location, and demographic details, you can apply clustering algorithms to identify distinct customer segments. This information can then be used to personalize marketing campaigns, recommend relevant products, or tailor the user experience to different customer groups.

The dataset

We will use the e-commerce dataset, which can be downloaded as a CSV file from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/352/online+retail. It contains data for all the transactions that occurred between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retailer.

The dataset contains the following columns:

  • InvoiceNo: A 6-digit integral number uniquely assigned to each transaction
  • StockCode: A 5-digit integral number...

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