Unsupervised Machine Learning

Unsupervised learning finds hidden patterns or intrinsic structures in input data. Unlike supervised learning, there are no labeled outputs. Common tasks include clustering and dimensionality reduction.

The K-Means demo loads a tiny ONNX model trained on real Iris petal measurements (see scripts/export_tutorial_onnx.py).

K-Means Clustering

K-Means groups data into 'k' non-overlapping subgroups (clusters) distinct from each other. It tries to make the intra-cluster data points as similar as possible while keeping the clusters as different as possible.

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.predict(X)

K-Means Step-by-Step

Watch K-Means converge step by step. Initialize centroids, then step through assignment and update phases. Choose a preset dataset or draw your own points by clicking on the canvas.

Principal Component Analysis (PCA)

PCA finds the directions of maximum variance in high-dimensional data and projects it onto a lower-dimensional subspace — preserving as much information as possible. It's used for visualisation, noise reduction, and speeding up downstream models.

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)   # PCA needs normalised data

pca = PCA(n_components=2)
X_2d = pca.fit_transform(X_scaled)

print(pca.explained_variance_ratio_)  # % variance per component

Hidden Markov Models

A Hidden Markov Model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states.

Knowledge Check

Test your understanding — pick the best answer, then click Check.