This course will help you gain a clear understanding of factor analysis. It is a cookie cutter technique that will teach you how to solve factor extraction. If you aspire to or want to work in data analysis, taking this course will give you the skills and knowledge you require to further your career opportunities. Getting certified will make you a marketable candidate with a competitive advantage over your peers. 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!
Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.
This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).
This course consists of the following modules:
From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.
At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed the course.
Successful candidates will be awarded a certificate in Machine Learning – Factor Analysis.
Once you successfully complete the course in Machine Learning – Factor Analysis, you will gain a recognised qualification that will prove your expertise in factor analysis. You can use this certification to expand your career opportunities and find the desired job role. You will be eligible for a higher pay, or incentives put forth by the organisation. A career with bright prospects await you with the completion of this course. According to www.payscale.uk, some of the key job positions along with the average UK salary per annum you can go for after completing this course will be as follows.
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
PLEASE NOTE: We do not provide any software with this course
|Machine Learning – Factor Analysis||FREE||00:00:00|
|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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