ML-For-Beginners/Clustering/1-Visualize

Introduction to Clustering

While you're studying Machine Learning with Clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.

Pre-lecture quiz

Clustering is a type of unsupervised learning that presumes that a dataset is unlabelled. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. Clustering is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.

✅ Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes 🧦👕👖🩲. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos.

Introduction

Scikit-Learn offers a large array of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-Learn and their appropriate use cases:

Method name	Use case
K-Means	general purpose, inductive
Affinity propagation	many, uneven clusters, inductive
Mean-shift	many, uneven clusters, inductive
Spectral clustering	few, even clusters, transductive
Ward hierarchical clustering	many, constrained clusters, transductive
Agglomerative clustering	many, constrained, non Euclidan distances, transductive
DBSCAN	non-flat geometry, uneven clusters, transductive
OPTICS	non-flat geometry, uneven clusters with variable density, transductive
Gaussian mixtures	flat geometry, inductive
BIRCH	large dataset with outliers, inductive

🎓 Let's unpack some vocabulary:

• 'transductive' vs. 'inductive'
• 'non-flat' vs. 'flat' geometry
• 'distances'
• 'constrained'
• 'density'

Preparation

Open the notebook.ipynb file in this folder and append the song data

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🚀Challenge

Add a challenge for students to work on collaboratively in class to enhance the project

Optional: add a screenshot of the completed lesson's UI if appropriate

Post-lecture quiz

Review & Self Study

Assignment: Assignment Name