Machine Learning Classification Algorithms using MATLAB [Video]

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Machine Learning Classification Algorithms using MATLAB [Video]

Nouman Azam

Learn to Implement Classification Algorithms in One of the Most Power Tool used by Scientists and Engineer.

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Video Details

ISBN 139781788992213
Course Length6 hours and 53 minutes

Video Description

This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.

Segment 1: Instructor and Course Introduction
Segment 2: MATLAB Crash Course
Segment 3: Grabbing and Importing Dataset
Segment 4: K-Nearest Neighbor
Segment 5: Naive Bayes
Segment 6: Decision Trees
Segment 7: Discriminant Analysis
Segment 8: Support Vector Machines
Segment 9: Error Correcting Output Codes
Segment 10: Classification with Ensembles
Segment 11: Validation Methods
Segment 12: Evaluating Performance

Style and Approach

This course is really good for a beginner. It will help you to start from the ground up and move on to more complicated areas. You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and research experience,

Table of Contents

Instructor and Course 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 scaling issue and copying a learned model (4)
Types of Properties (5)
Building a model with subset of classes, missing values and instances weights (6)
Properties of KNN
Naive Bayes
Intuition of Naive Bayesian Classification
Naive Bayes in MATLAB
Building a model with categorical data
A Final note on Naive Bayesian 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 Classifier 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 arguments
Properties of ECOC model
Classification with Ensembles
Ensembles in MATLAB
Properties of Ensembles
Validation Methods
Cross validation options (Part 1)
Cross validation 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 (10)

What You Will Learn

  • Use machines learning algorithms confidently in MALTAB
  • Build classification learning models and customize them based on the datasets
  • Compare the performance of different classification algorithms
  • Learn the intuition behind classification algorithms
  • Create automatically generated reports for sharing your analysis results with friends and colleague

Authors

Table of Contents

Instructor and Course 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 scaling issue and copying a learned model (4)
Types of Properties (5)
Building a model with subset of classes, missing values and instances weights (6)
Properties of KNN
Naive Bayes
Intuition of Naive Bayesian Classification
Naive Bayes in MATLAB
Building a model with categorical data
A Final note on Naive Bayesian 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 Classifier 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 arguments
Properties of ECOC model
Classification with Ensembles
Ensembles in MATLAB
Properties of Ensembles
Validation Methods
Cross validation options (Part 1)
Cross validation 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 (10)

Video Details

ISBN 139781788992213
Course Length6 hours and 53 minutes
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