← Machine learning: intermediate and advanced
Lesson 3 of 4
Unsupervised learning
Watch, read the card, then practise. Clustering · Dimensionality reduction
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Meanwhile, the notes below and the 60-second cards cover the same ideas.
Practise this topicClustering
Clustering groups similar points without labels. k-means finds k groups around centres.
Remember: Scale features first: clustering uses distances.
Dimensionality reduction
Dimensionality reduction squeezes many features into a few, keeping most of the information.
Remember: Standardise before PCA, or big-unit features dominate.
Try the example in Code Lab
import numpy as np cluster = np.array([[1, 2], [3, 4], [5, 6]]) print(cluster.mean(axis=0).tolist())