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.

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.

Python Implementation

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

Live Demo: Iris Sort

ONNX

Sorts Iris flowers into clusters based on petal dimensions.

Petal Length (cm): 1.4

Petal Width (cm): 0.2

Principal Component Analysis (PCA)

PCA is a dimensionality reduction technique that transforms a large set of variables into a smaller one that still contains most of the information in the large set.

It reduces the number of variables of a data set, while preserving as much information as possible.

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.