Machine Learning – Factor Analysis


Factor extraction using PCA in Excel, R, and Python.

This ’Factor Analysis’ online training course will help you understand Factor Analysis and its link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine Learning. Supplemental Materials included!

Length: 1 hr 45 min

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Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect. In this course, you will follow along with expert instructors to learn about topics such as Mean & Variance, Eigen Vectors, Covariance Matrices, and so much more!

Course Highlights:

  • Understand & Analyze Principal Components
  • Use Principal Components for dimensionality reduction and exploratory factor analysis
  • Apply PCA to explain the returns of a technology stock like Apple®
  • Build Regression Models with Principal Components in Excel, R, & Python

Course Requirements:

  • No statistics background required. Everything is built up from basic math.
  • The models are implemented in Excel, R, & Python. Install these environments to follow along with the demos.

Target Audience:

  • Data analysts who want to move from summarizing data to explaining and prediction
  • Folks aspiring to be data scientists
  • Any business professionals who want to apply Factor Analysis and Linear Regression to solve relevant problems


Sample clip


Chapter 01: Introduction

Lesson 01: You, This Course, & Us!

Chapter 02: Factor Analysis & PCA

Lesson 01: Factor Analysis & the Link to Regression

Lesson 02: Factor Analysis & PCA

Chapter 03: Basic Statistics Required for PCA

Lesson 01: Mean & Variance

Lesson 02: Covariance & Covariance Matrices

Lesson 03: Covariance vs Correlation

Chapter 04: Diving into Principal Components Analysis

Lesson 01: The Intuition Behind Principal Components

Lesson 02: Finding Principal Components

Lesson 03: Understanding the Results of PCA – Eigen Values

Lesson 04: Using Eigen Vectors to find Principal Components

Lesson 05: When not to use PCA

Chapter 05: PCA in Excel

Lesson 01: Setting up the data

Lesson 02: Computing Correlation & Covariance Matrices

Lesson 03: PCA using Excel & VBA

Lesson 04: PCA & Regression

Chapter 06: PCA in R

Lesson 01: Setting up the data

Lesson 02: PCA and Regression using Eigen Decomposition

Lesson 03: PCA in R using packages

Chapter 07: PCA in Python

Lesson 01: PCA & Regression in Python

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum