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Machine Learning Classification Algorithms using MATLAB
Course and Instructor Introduction
Applications of Machine Learning (1:35)
Why use MATLAB for Machine Learning (3:13)
Meet Your Instructor (1:24)
Course Outlines (1:43)
MATLAB Crash Course
MATLAB Pricing and Online Resources
MATLAB GUI (4:57)
Some common Operations (11:56)
Grabbing and Importing a Dataset
Data Types that We May Encounter (6:02)
Grabbing a dataset (2:20)
Importing Data into MATLAB (9:35)
Understanding the Table Data Type (11:36)
K-Nearest Neighbor
Nearest Neighbor Intuition (9:19)
Nearest Neighbor in MATLAB (9:39)
Learning KNN model with features subset and with non-numeric data (10:48)
Dealing with scalling issue and copying a learned model (3:32)
Types of Properties (11:22)
Building a model with subset of classes, missing values and instances weights (6:58)
Properties of KNN (5:08)
Naive Bayes
Intuition of Naive Bayesain Classification (15:43)
Naive Bayes in MATLAB (10:34)
Building a model with categorical data (6:24)
A Final note on Naive Bayesain Model (3:00)
Decision Trees
Intuition of Decision Trees (9:01)
Decision Trees in MATLAB (5:35)
Properties of the Decision Trees (14:24)
Node Related Properties of Decision Trees (9:20)
Properties at the Classifer Built Time (7:25)
Discriminant Analysis
Intuition of Discriminant Analysis (6:44)
Discriminant Analysis in MATLAB (4:41)
Properties of the Discriminant Analysis Learned Model in MATLAB (7:03)
Support Vector Machines
Intuition of SVM Classification (7:41)
SVM in MATLAB (12:34)
Properties of SVM learned model in MATLAB (12:46)
Error Correcting Output Codes
Intuition of ECOC (5:29)
ECOC in Matlab (9:15)
ECOC name, value arguemnts (12:59)
Properties of ECOC model (4:51)
Classification with Ensembles
Ensembles in MATLAB (12:33)
Properties of Ensembles (5:28)
Validation Methods
Cross validition options (Part 1) (10:07)
Cross validition options (Part 2) (10:08)
Performance Evaluation
Making Predictions with the Models (8:06)
Determining the classification loss (7:59)
Classification Margins and Edge (15:23)
Classification Loss, Margins, Predictions and Edge for cross validated models (10:49)
Comparing two classifiers with holdout (13:16)
Computing Confusion Matrix (7:38)
Generating ROC Curve (9:45)
Generating ROC Curve based on the testing data (8:45)
More Customization and information while generating ROC (6:25)
Computing Accuracy, Error Rate, Specificity and Sensitivity (5:10)
Intuition of Naive Bayesain Classification
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