By Kardi Teknomo, PhD.

What is PCA?

The Principal Component Analysis (PCA) is a classical multivariate statistics technique, one of the most useful data modeling for feature selection, dimensional reduction and visualization. Using PCA, many variables of your data can be reduced into a few components. The success of PCA lies on its capability to capture more significant underlying-structure of the data, and at the same time, it removes the noise or trivial redundancies in the data. By extracting the maximum variance in the data and removing the correlation at the same time, PCA is able to extract the information that are invariant and insensitive to variation within each class of data.

This unique PCA tutorial would give you a very gentle introduction to a feature extraction, and dimensional reduction. In feature extraction, it would help you selecting certain features, which would contain most information content in the data. In dimensional reduction, the original dataset is transformed into a new dataset such that the number of variables in the new dataset is reduced to be much lesser than the number of variables in the original dataset. When we reduce the number of dimension, it simplifies the process to find the pattern of association and you may probably visualize the pattern. By reading and do the practice of the numerical examples of this tutorial to the end, at least you will be able to ready to read other more advanced PCA books.

Your Benefit

You have read our sample FREE tutorial this far. Our tutorial has given you FAST and SIMPLE understanding about the topic. Unlike many other books and tutorials out there, we only serve you the main course. Our tutorials always have comprehensive examples that teach you step by step on how to do it. Do you want to gain BETTER understanding about this topic? Do you want to know more on how to do it on your own? You need to download this tutorial. You need to download the Excel Add-In companion of this tutorial.

In this e-book, you will learn how to perform training on Principal Component Analysis (PCA), learning in much detail on how to perform PCA based on Covariance matrix, Correlation matrix and Singular Value Decomposition through examples. The e-book also discussed about the strengths and weaknesses of PCA, interpreting the PCA results, and deciding on the number of component. What is more, the e-book also comes with a spreadsheet companion file, python code for your own practice worksheets and the complete solutions. Alternatively, you can also purchase a bundle of PCA Excel Add-In with this tutorial. This e-book PCA tutorial is designed to enhance your understanding of PCA in very gentle way but fast to learn. With this e-book as your guide, learning PCA becomes easy steps and enjoyable practice moment.

Why do you need to download PCA e-book and PCA Excel Add-In NOW?

• Gain full understanding. Your memory about your new understanding about this fascinating topic is still fresh. If you download it now, you will easily understand the detail of the tutorial. If you do it next time, you might have forgotten the understanding that has gained today.
• Save your learning time. Your time is very valuable. It saves a lot of your time to do self study on this topic.
• Understanding this topic will enhance your study, your work, and your research. Eventually you can develop it further to get more money for your future.
• Tired of ads? By purchasing the FULL tutorial, you will be able to read tutorial in a very nice PDF format without advertising. You can zoom in and out to see the formulas clearly. You can read in any computing device (phone, tablet, PC, laptop, Mac, iPad).
• The tutorial also comes in e-book together with the companion file. This option will give you an eye opener to this fascinating topic. It enhances your understanding. The complete tutorial will be your self-study guide to improve your learning process. You can read it off-line anywhere anytime even when you don’t have internet connection.

What you will get when you download it NOW?

In this Principal Component Analysis Tutorial e-book you will learn, you will learn the following fascinating topics in a very SHORT TIME:

• What is PCA?
• Why do we need PCA?
• How does PCA works?
• What are the PCA algorithms?
• PCA Numerical Examples
• How to compute Mean corrected data matrix?
• How to calculate Standardized Data?
• How to calculate Covariance Matrix from your data?
• How to calculate Correlation Matrix from your data?
• How to obtain Principal Component Analysis (PCA) from Covariance Matrix?
• How to obtain Principal Component Analysis (PCA) from Correlation Matrix?
• How to obtain Principal Component Analysis (PCA) based on Singular Value Decomposition (SVD)?
• Free PCA online Interactive Programs
• PCA in Microsoft Excel (using PCA Excel Add-In)
• PCA in Python
• How many PCA components should we use?
• How to interpret PCA results?
• What are the applications of PCA?
• What are the strength and Weaknesses of PCA?
• Brief on Linear Algebra

See the comparison table below for the options.

Features	Options
		Only PCA Tutorial/strong>	Bundle PCA Tutorial and PCA Excel Add-In (Best Buy)
MS Excel Workbook with complete solutions for PCA training and results for your own practices.
Free Python Code
Nice format of e-book of Principal Component Analysis Tutorial in PDF format
PCA Excel Add In (for Excel 2016 and above)
Price		ONLY $12	ONLY $50
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