Machine Learning Specialization · Unsupervised Learning, Recommenders, Reinforcement Learning

PCA Exploratory Data Analysis

A dimensionality-reduction milestone using PCA to reveal hidden structure that is not visible from raw pairwise feature plots.

Principal Component AnalysisDimensionality reductionVariance explained2D/3D projectionHidden cluster discovery

GitHub

What this project is about

The PCA milestone is about seeing structure that raw dimensions hide. Pairwise plots and correlations may look noisy, but projecting data onto principal components can reveal separable patterns and hidden clusters.

Implementation focus

The main lesson was that dimensionality reduction is not only compression. It is also an exploratory tool that helps humans understand high-dimensional data before modeling.

Key results and artifacts

Toy example: first principal component retained about 99.24% variance
High-dimensional example: 500 samples × 1000 features
2D/3D PCA projections revealed clear clusters

What I learned

The key learning was that projection is not only compression; it is also a way to see the geometry of data.

Milestone deep dive

What this project proves in my learning path.

A dimensionality-reduction milestone using PCA to reveal hidden structure that is not visible from raw pairwise feature plots. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerfeature matrices and geometric structure

Model layerunsupervised algorithm or probabilistic model

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerclusters, projections, compressed images, or outliers

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

Principal Component AnalysisDimensionality reductionVariance explained2D/3D projectionHidden cluster discovery

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

The key learning was that projection is not only compression; it is also a way to see the geometry of data.

Important outputs and evidence

Toy example: first principal component retained about 99.24% variance
High-dimensional example: 500 samples × 1000 features
2D/3D PCA projections revealed clear clusters

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.