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.
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.
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.
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 (payscale.com). When you complete this Machine Learning – Factor Analysis, you could fulfil any of the following roles:
|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|