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Machine Learning - Factor Analysis

Indian : Rs.1918
International : $30
INTRODUCTION
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!
OBJECTIVE
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!
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
CONTENT
Chapter I: Introduction
  • Lesson I: You, This Course, & Us!

Chapter II: Factor Analysis & PCA
  • Lesson I: Factor Analysis & the Link to Regression
  • Lesson II: Factor Analysis & PCA

Chapter III: Basic Statistics Required for PCA
  • Lesson I: Mean & Variance
  • Lesson II: Covariance & Covariance Matrices
  • Lesson III: Covariance vs Correlation

Chapter IV: Diving into Principal Components Analysis
  • Lesson I: The Intuition Behind Principal Components
  • Lesson II: Finding Principal Components
  • Lesson III: Understanding the Results of PCA – Eigen Values
  • Lesson IV: Using Eigen Vectors to find Principal Components
  • Lesson V: When not to use PCA

Chapter V: PCA in Excel
  • Lesson I: Setting up the data
  • Lesson II: Computing Correlation & Covariance Matrices
  • Lesson III: PCA using Excel & VBA
  • Lesson IV: PCA & Regression

Chapter VI: PCA in R
  • Lesson I: Setting up the data
  • Lesson II: PCA and Regression using Eigen Decomposition
  • Lesson III: PCA in R using packages

Chapter VII: PCA in Python
  • Lesson I: PCA & Regression in Python
LENGTH
1 hr 45 min
PRESENTER INFO
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
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