Principal Component Analysis Tutorial Part 1 | Python Machine Learning Tutorial Part 3 смотреть онлайн

Principal Component Analysis Tutorial | Python Machine Learning Tutorial Part 3
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Machine learning algorithm typically finds the pattern and relationships in data without human intervention but the data that the machine learning algorithm had to deal with are usually very high dimensional.
Welcome back to another session of Machine Learning Algorithms in Python tutorial powered by Acadgild. In the previous video, you have learned the linear regression. If you have missed the previous, please check the links as follows.
Simple Linear Regression - https://www.youtube.com/watch?v=iL_iWFSzjK8&t=7s
Implementing Linear Regression in Python - https://www.youtube.com/watch?v=M1mzE1IT-Is&t=225s

In this machine learning tutorial, you will be able to learn Principal Component Analysis in python. Principal Component Analysis is a data pre-processing technique that allows the data to be transformed from higher dimensional space to a lower dimensional space in such a way that information that is crucial to drawing conclusions about the data is not lost.

So, What Exactly is Principal Component Analysis (PCA)?
• Principal Component Analysis (PCA) is a dimensionally-reduction technique that is often used to transform a high-dimensional dataset into smaller-dimensional subspace
• PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.

What are Principal Components?
• Directions in which the data has the most variance – directions in which the data is most spread out
• Mathematically, Eigenvectors of the symmetric covariance matrix of the original dataset
• Each Eigenvector has the corresponding Eigenvalue. The Eigenvalue is a scalar that explains how much variance there is in the corresponding Eigenvector direction.

Applications of Principal Component Analysis (PCA)
• Compression
• Visualization of high dimensional data
• Speeding up of machine learning algorithms
• Reducing noise from data

Using Principal Component Analysis (PCA) for Compression:
Once Eigenvectors are computed, compress the dataset by ordering k eigenvectors according to largest eigenvalues and compute Axk
Reconstruct from the compressed version. We can reconstruct the data back by using inverse transformation mathematically represented by Axk x k.T

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