# ML & Dimensionality Reduction: Performing Principal Component Analysis

Machine Learning    |    Intermediate
• 11 videos | 1h 15m 30s
• Includes Assessment
Rating 4.4 of 36 users (36)
Principal component analysis (PCA) is a must-know pre-processing technique for anyone working with machine learning (ML). Used to process data fed into ML models, PCA is useful in many scenarios, such as exploratory data analysis, dimensionality reduction, and latent feature extraction. Use this course to learn the basic intuition behind principal component analysis along with how to use PCA. Start by visualizing how principal components work. Then, examine how they can be computed mathematically using the eigenvectors and eigenvalues of the covariance matrix of the data. As you advance, manually compute principal components, view the re-oriented data, and compare this result with the principal components computed. Lastly, use PCA for dimensionality reduction to train a classification model. When you're done, you'll have the skills and knowledge to use PCA to build more robust machine learning models.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Recall the use of matrix operations to represent linear transformations
Recall the intuition behind principal component analysis
Define principal components and their uses
Define eigenvalues and eigenvectors
Mathematically compute principal components
• Compute eigenvalues and eigenvectors
Perform principal component analysis
Build a baseline model using logistic regression
Build a logistic regression model using principal components
Summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Change of Basis, The Intuition behind PCA
• 4.  An Explanation of Principal Components
• 5.  A Quick Exploration of Eigenvectors and Eigenvalues
• 6.  Computing Principal Components
• 7.  Computing Eigenvectors and Eigenvalues
• 8.  Calculating Principal Components
• 9.  Building a Baseline Classification Model
• 10.  Training a Model Using Principal Components
• 11.  Course Summary

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