Data Analysis and Machine Learning: Preprocessing and Dimensionality Reduction
Contents
Reducing the number of degrees of freedom, overarching view
Preprocessing our data
More preprocessing
Simple preprocessing examples, Franke function and regression
Simple preprocessing examples, breast cancer data and classification, Support Vector Machines
More on Cancer Data, now with Logistic Regression
Why should we think of reducing the dimensionality
Basic ideas of the Principal Component Analysis (PCA)
Introducing the Covariance and Correlation functions
Correlation Function and Design/Feature Matrix
Covariance Matrix Examples
Correlation Matrix
Correlation Matrix with Pandas
Correlation Matrix with Pandas and the Franke function
Classical PCA Theorem
Prof of the PCA Theorem
Getting started with PCA
Principal Component Analysis
PCA and scikit-learn
More on the PCA
Incremental PCA
Randomized PCA
Kernel PCA
LLE
Other techniques
Prof of the PCA Theorem
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