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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

Dataset overview

This dataset provides us with a wealth of information about Apple’s stock (traded under AAPL) over the past decade, starting from the year 2010. This data is incredibly valuable because it can help us develop forecasting algorithms to predict the future price of Apple’s stock, which is crucial for making investment decisions. The data in this set has been collected and aggregated from 25 different stock exchanges.

To effectively use this data for forecasting, we need to understand the key elements: the features that influence our target, which is predicting stock prices.

The dataset includes five important values that indicate how the stock price changes over a specific period of time, which is typically one day, but it could also be one week or one month. These values are:

  • Open: This is the stock price at the beginning of the trading day.
  • Close: This is the stock price at the end of the trading day.
  • High: This value...

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