Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos

Introduction

When people hear “machine learning” they often feel overwhelmed by the many algorithms. This article provides a clear roadmap for selecting methods, referencing the Scikit‑learn guide and a blog by Li Hui.

Supervised vs Unsupervised Learning

Supervised learning works with labeled data, covering regression and classification. Unsupervised learning deals with unlabeled data, focusing on dimensionality reduction and clustering.

Dimensionality Reduction – PCA

Principal Component Analysis (PCA) reduces high‑dimensional data to lower dimensions by finding the directions (principal components) that capture the most variance. The article demonstrates PCA with an interactive 2‑D plot where a white line represents the principal component and blue lines show projections.

Clustering Methods

Clustering groups similar data points without labels. The article covers hierarchical clustering, K‑Means, and DBSCAN.

Hierarchical Clustering

Start with each point as its own cluster.

Iteratively merge the two closest clusters.

Distance between clusters is defined by the nearest pair of points.

Repeat until a single hierarchy is formed.

K‑Means

Randomly select K seed centroids (e.g., K=3).

Assign each point to the nearest centroid.

Recompute centroids as the mean of assigned points.

Repeat assignment and centroid update until convergence.

Issues include choosing K, sensitivity to initial centroids, and difficulty with non‑convex shapes.

DBSCAN

Density‑Based Spatial Clustering of Applications with Noise (DBSCAN) groups points based on density, requiring two parameters: Eps (neighborhood radius) and MinPts (minimum points to form a core point). It can discover arbitrarily shaped clusters and identify noise without needing K.

Regression Algorithms

Linear regression models the relationship between one or more independent variables and a continuous target by minimizing squared error. For higher accuracy, ensemble methods such as Random Forest, Gradient Boosting, or neural networks are suggested; for speed, decision trees or simple linear regression suffice.

Classification Algorithms

Support Vector Machine (SVM) finds a hyperplane that maximally separates classes; kernel tricks enable non‑linear boundaries.

Different kernel methods produce varied classification effects.

Decision trees provide interpretable rules and can handle both classification and regression.

Logistic regression, despite its name, is a binary classifier similar to linear SVM; the article highlights the distinction between logistic regression and linear SVM.

Naïve Bayes works well on large datasets and can be effective for text classification.

K‑Nearest Neighbors (KNN) classifies a point by majority vote among its K nearest neighbors; the article illustrates K=3 with an interactive plot.

Conclusion

The article emphasizes that understanding the problem type and data characteristics guides the choice of algorithm, and interactive visual demos (available in the author’s CodePen collection) help illustrate each method.