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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

Data exploration of the Amazon review dataset using the free version of ChatGPT

ChatGPT premium version has a code interpreter, but first, we will use the free version of ChatGPT. We will craft our initial prompt carefully to include all the features, but we will instruct it to wait for user feedback after providing code for each feature.

Feature 1: Loading the dataset

Let’s craft our initial prompt to load the dataset.

[Prompt]

I want to explore the Amazon product review dataset thoroughly using various statistical and visualization techniques (TAG 1.1), which consists of the following fields (PIC 2.3):

  • marketplace (string): The location of the product.
  • customer_id (string): A unique identifier for customers.
  • review_id (string): A unique identifier for reviews.
  • product_id (string): A unique identifier for products.
  • product_parent (string): A parent product identifier.
  • product_title (string): The title of the reviewed...

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