Machine Learning
Machine Learning in R and Python
Embedded Files
Part 9 - Dimensionality Reduction
Section 43 - Principal Component Analysis (PCA)
Principal Component Analysis (PCA) Intuition
Principal Component Analysis in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Applying PCA
Fitting Logistic Regression to the Training set
Predicting the Test set results
Making the Confusion Matrix
Visualising the Training set results
Visualising the Test set results
Principal Component Analysis in R
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Applying PCA
Fitting SVM to the Training set
Predicting the Test set results
Making the Confusion Matrix
Visualising the Training set results
Visualising the Test set results
Section 44 - Linear Discriminant Analysis (LDA)
Linear Discriminant Analysis (LDA) Intuition
Linear Discriminant Analysis in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Applying LDA
Fitting Logistic Regression to the Training set
Predicting the Test set results
Making the Confusion Matrix
Visualising the Training set results
Visualising the Test set results
Linear Discriminant Analysis in R
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Applying LDA
Fitting SVM to the Training set
Predicting the Test set results
Making the Confusion Matrix
Visualising the Training set results
Visualising the Test set results
Section 45 - Kernel PCA
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Applying Kernel PCA
Fitting Logistic Regression to the Training set
Predicting the Test set results
Making the Confusion Matrix
Visualising the Training set results
Before Kernal PCA
After Kernal PCA
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Applying Kernel PCA
Fitting Logistic Regression to the Training set
Predicting the Test set results
Making the Confusion Matrix
Visualising the Training set results
Before Kernal PCA
After Kernal PCA
Section 46 - Part Recap
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