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Machine Learning Classification Algorithms using MATLAB
Course and Instructor Introduction
Applications of Machine Learning
Why use MATLAB for Machine Learning
Meet Your Instructor
Course Outlines
MATLAB Crash Course
MATLAB Pricing and Online Resources
MATLAB GUI
Some common Operations
Grabbing and Importing a Dataset
Data Types that We May Encounter
Grabbing a dataset
Importing Data into MATLAB
Understanding the Table Data Type
K-Nearest Neighbor
Nearest Neighbor Intuition
Nearest Neighbor in MATLAB
Learning KNN model with features subset and with non-numeric data
Dealing with scalling issue and copying a learned model
Types of Properties
Building a model with subset of classes, missing values and instances weights
Properties of KNN
Naive Bayes
Intuition of Naive Bayesain Classification
Naive Bayes in MATLAB
Building a model with categorical data
A Final note on Naive Bayesain Model
Decision Trees
Intuition of Decision Trees
Decision Trees in MATLAB
Properties of the Decision Trees
Node Related Properties of Decision Trees
Properties at the Classifer Built Time
Discriminant Analysis
Intuition of Discriminant Analysis
Discriminant Analysis in MATLAB
Properties of the Discriminant Analysis Learned Model in MATLAB
Support Vector Machines
Intuition of SVM Classification
SVM in MATLAB
Properties of SVM learned model in MATLAB
Error Correcting Output Codes
Intuition of ECOC
ECOC in Matlab
ECOC name, value arguemnts
Properties of ECOC model
Classification with Ensembles
Ensembles in MATLAB
Properties of Ensembles
Validation Methods
Cross validition options (Part 1)
Cross validition options (Part 2)
Performance Evaluation
Making Predictions with the Models
Determining the classification loss
Classification Margins and Edge
Classification Loss, Margins, Predictions and Edge for cross validated models
Comparing two classifiers with holdout
Computing Confusion Matrix
Generating ROC Curve
Generating ROC Curve based on the testing data
More Customization and information while generating ROC
Computing Accuracy, Error Rate, Specificity and Sensitivity
Properties of the Discriminant Analysis Learned Model in MATLAB
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