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Machine Learning Factor Analysis

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Course Description:

This excellent Machine Learning – Factor Analysis course will help you to understand Factor Analysis and its link to linear regression. If you’re someone who works in analytics, or with big data, this Machine Learning – Factor Analysis course will show you how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine Learning. If you’re someone who needs to get to grips with machine learning, this course is for you, and it will help you to grasp the theory underlying factor analysis.

Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.

What skills will I gain?

  • Understand & Analyse 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

What are the requirements?

  • You must be 16 or over
  • You should have a basic understanding of English, Maths and ICT
  • You will need a computer or tablet with internet connection (or access to one)

Meet the Instructor:

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

Course outline:

  • Module 01: Introduction
  • Module 02: Factor Analysis & PCA
  • Module 03: Basic Statistics Required for PCA
  • Module 04: Diving into Principal Components Analysis
  • Module 05: PCA in Excel
  • Module 06: PCA in R
  • Module 07: PCA in Python

How will I be assessed?

  • You will have one assignment. Pass mark is 65%.
  • You will only need to pay £19 for assessment.
  • You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days.

What Certification am I going to receive?

Those who successfully pass this course will be awarded a Machine Learning – Factor Analysis certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.

What careers can I get with this qualification?

Once you have completed this Machine Learning – Factor Analysis course you will have desirable skills. You could go on to further study of this topic, or could gain entry level employment in analytics or big data. These roles often command a high salary, for example, the average salary of a Data Scientist in the UK is £43,318, and this will go up with experience ( When you complete this Machine Learning – Factor Analysis, you could fulfil any of the following roles:

  • Data Scientist
  • Big Data Specialist
  • Data Architect
  • Data Analyst

Key Features

Gain an accredited UK qualification

Access to excellent quality study materials

Learners will be eligible for TOTUM Discount Card

Personalized learning experience

One year’s access to the course

Support by phone, live chat, and email

Course Curriculum Total Units : 19
1: Introduction
1. You, This Course, & Us!
2: Factor Analysis & PCA
1. Factor Analysis & the Link to Regression
2. Factor Analysis & PCA
3: Basic Statistics Required for PCA
1. Mean & Variance
2. Covariance & Covariance Matrices
3. Covariance vs Correlation
4: Diving into Principal Components Analysis
1. The Intuition Behind Principal Components
2. Finding Principal Components
3. Understanding the Results of PCA – Eigen Values
4. Using Eigen Vectors to find Principal Components
5. When not to use PCA
5: PCA in Excel
1. Setting up the data
2. Computing Correlation & Covariance Matrices
3. PCA using Excel & VBA
4. PCA & Regression
6: PCA in R
1. Setting up the data
2. PCA and Regression using Eigen Decomposition
3. PCA in R using packages
7: PCA in Python
1. PCA & Regression in Python
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