In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - Classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia. Hope you're hungry!
Classification is a form of supervised learning that bears a lot in common with Regression techniques. If machine learning is all about assigning names to things via datasets, then classification generally falls into two groups: binary classification and multiclass classfication.
Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
🎥 Click the image above for a video: MIT's John Guttag introduces Classification
Before working to clean the data and prepare it for analysis, it's useful to understand several of the algorithms that you will use.
- Support-vector machines
- Naive Bayes
- Decision trees
- K-nearest neighbor algorithm
✅ Knowledge Check - use this moment to stretch students' knowledge with open questions