Date: 1st November 2023
💡 Classification helps us to group data into predefined classes. Logistic Regression is a linear model used for binary classification, while K-means is an unsupervised clustering algorithm that categorizes data into clusters. Support Vector Machines (SVM) find an optimal hyperplane to separate data, and Decision Trees use a tree-like structure to make decisions based on feature attributes. Come to this session to explore and implement these four well-known classification methods! 💡
You can access our demonstration notebook here: 📘 Tutorial 3 Notebook
The folder contains notebooks for Logistic Regression, SVMs, Decision Trees and K-Means. The solutions are available in the same folder.
You can access our slides here: 💻 Tutorial 3 Slides
The recording from this session is available here: 🎤 Tutorial 3 Recording
The DOXA Challenge notebook can be found here: 🏆 DOXA Challenge 1
This is now open for submissions! Let's see how well your models can perform🙌🏼
This is the end of the Classical Machine Learning section of the series. We will continute next half term on Deep Learning 🥳
Hope you have a great reading week! Please join our WhatsApp group chat through this link.