Date: 25th October 2023
💡 Linear regression in simple terms is finding the line of best fit given a set of data points that loosely follow a linear pattern. One way of doing this is by minimising the loss (difference between actual and computed value), making it useful for predicting numerical outcomes based on input features. Polynomial regression extends linear regression by allowing the model to capture nonlinear relationships between variables, using polynomial functions to fit the data more flexibly. 💡
You can access our demonstration notebook here: 📘 Tutorial 2 Notebook
The solution is available in the same folder.
You can access our slides here: 💻 Tutorial 2 Slides
The recording from this session is available here: 🎤 Tutorial 2 Recording