Unsupervised Learning

What is Unsupervised Learning?#

Unlike supervised learning, unsupervised learning works with unlabeled data. The goal is to discover hidden patterns and structures.

The Challenge

No labels means no "correct answers" to learn from. The algorithm must find structure on its own.

Types of Unsupervised Learning#

Feature	Type	Goal
Clustering	Group similar items	Customer segments
Dimensionality Reduction	Reduce features	Data visualization
Anomaly Detection	Find outliers	Fraud detection

K-Means Clustering#

The most popular clustering algorithm:

python

from sklearn.cluster import KMeans

# Fit model
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)

# Get cluster assignments
labels = kmeans.labels_
centers = kmeans.cluster_centers_

PCA for Dimensionality Reduction#

python

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X)

print(f"Variance explained: {pca.explained_variance_ratio_.sum():.2%}")

Key Takeaways#

Remember

Unsupervised learning finds hidden structure in data. Start with K-Means for clustering, PCA for dimensionality reduction.

Feature	Type	Goal	Example
Clustering	Group similar items	Customer segments
Dimensionality Reduction	Reduce features	Data visualization
Anomaly Detection	Find outliers	Fraud detection

Feature

Type

Goal

Example

Clustering

Group similar items

Customer segments

Dimensionality Reduction

Reduce features

Data visualization

Anomaly Detection

Find outliers

Fraud detection

What is Unsupervised Learning?#

Types of Unsupervised Learning#

K-Means Clustering#

PCA for Dimensionality Reduction#

Key Takeaways#

Ready to level up your skills?

Unsupervised Learning

What is Unsupervised Learning?#

Types of Unsupervised Learning#

K-Means Clustering#

PCA for Dimensionality Reduction#

Key Takeaways#

Ready to level up your skills?