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• Get comfortable using resources and AI tools to quickly jump into new technologies
• See how the machine learning models you develop can be used in applications

Convert your existing linear regression model into an API-accessible service using Flask. The API should accept input data and use the regression model (either by returning the result directly or using it to make a decision). Create a simple user interface in React that calls the API.

Try to ONLY use AI to write code! Talking to LLMs and getting them to do exactly what you want is a bit of an art and takes time to get used to. Practice communicating and instructing it clearly to get the results you want.

Get GitHub Copilot. Prepare your development environment with necessary libraries.

Create a basic Flask application and implement a prediction endpoint that accepts POST requests. Ensure your endpoint can process input data and return predictions.

Create a basic React application (I’d suggest using create-react-app) that collects the necessary model inputs from the user and displays the prediction.

If you’re getting errors connecting to the API it might be CORS.

Add error handling / input validation.

If you finish this early, try adding another endpoint that hosts a pre-trained GPT-2 model and predicts words.