Principal Component Analysis (PCA)

Explore Principal Component Analysis (PCA) to reduce dataset dimensionality while preserving important variance. Understand PCA's steps, applications like image compression, and how to implement PCA in Python using the Wine dataset. This lesson helps you visualize high-dimensional data and grasp PCA's role in simplifying data for machine learning.

We'll cover the following...

What is PCA?
- Sample answer
Applying PCA on images
- Sample answer
Implementing PCA
- Sample answer

Principal Component Analysis (PCA) is a powerful technique for reducing the dimensionality of datasets while preserving as much variance as possible. In this lesson, we'll explore the intuition behind PCA, understand its application in image compression, and use it to visualize high-dimensional data like the Wine dataset. Let’s get started.

What is PCA?

Your interviewer asks: What is Principal Component Analysis (PCA), and where is it useful?

This question is frequently asked at Meta, Apple, and DeepMind–especially for ML and CV-focused interviews. It is also relevant for ML Ops and tooling teams working on model deployment efficiency.

Sample answer

Principal Component Analysis (PCA) is a widely ...

1.Getting Started

2.Handling Diverse Real-World Data

3. Preparing and Transforming Data for Machine Learning Pipelines

4.Understanding Supervised Learning Algorithms

5.Understanding Unsupervised Learning Algorithms

6.Advanced Machine Learning Concepts

7.ML Applications and Deployment in the Real World

8.Responsible Machine Learning: Ethics, Fairness, and Privacy

9.ML Interview Preparation and Case Studies

Principal Component Analysis (PCA)

What is PCA?

Sample answer