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Machine Learning Classification Algorithms using MATLAB

Video Description

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

About This Video

  • You can confidently implement machine learning algorithms using MATLAB.
  • You can perform meaningful analysis on the data.

In Detail

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.

Table of Contents

  1. Chapter 1 : Instructor and Course Introduction
    1. Applications of Machine Learning 00:01:35
    2. Why use MATLAB for Machine Learning 00:03:13
    3. Meet Your Instructor 00:01:25
    4. Course Outlines 00:01:44
  2. Chapter 2 : MATLAB Crash Course
    1. MATLAB Pricing and Online Resources 00:05:01
    2. MATLAB GUI 00:04:58
    3. Some common Operations 00:11:56
  3. Chapter 3 : Grabbing and Importing a Dataset
    1. Data Types that We May Encounter 00:06:02
    2. Grabbing a dataset 00:02:20
    3. Importing Data into MATLAB 00:09:36
    4. Understanding the Table Data Type 00:11:36
  4. Chapter 4 : K-Nearest Neighbor
    1. Nearest Neighbor Intuition 00:09:19
    2. Nearest Neighbor in MATLAB 00:09:39
    3. Learning KNN model with features subset and with non-numeric data 00:10:49
    4. Dealing with scaling issue and copying a learned model (4) 00:03:32
    5. Types of Properties (5) 00:11:23
    6. Building a model with subset of classes, missing values and instances weights (6) 00:06:58
    7. Properties of KNN 00:05:08
  5. Chapter 5 : Naive Bayes
    1. Intuition of Naive Bayesian Classification 00:15:43
    2. Naive Bayes in MATLAB 00:10:34
    3. Building a model with categorical data 00:06:24
    4. A Final note on Naive Bayesian Model 00:03:00
  6. Chapter 6 : Decision Trees
    1. Intuition of Decision Trees 00:09:01
    2. Decision Trees in MATLAB 00:05:36
    3. Properties of the Decision Trees 00:14:24
    4. Node Related Properties of Decision Trees 00:09:21
    5. Properties at the Classifier Built Time 00:07:26
  7. Chapter 7 : Discriminant Analysis
    1. Intuition of Discriminant Analysis 00:06:44
    2. Discriminant Analysis in MATLAB 00:04:41
    3. Properties of the Discriminant Analysis Learned Model in MATLAB 00:07:04
  8. Chapter 8 : Support Vector Machines
    1. Intuition of SVM Classification 00:07:42
    2. SVM in MATLAB 00:12:35
    3. Properties of SVM learned model in MATLAB 00:12:47
  9. Chapter 9 : Error Correcting Output Codes
    1. Intuition of ECOC 00:05:30
    2. ECOC in MATLAB 00:09:16
    3. ECOC name, value arguments 00:13:00
    4. Properties of ECOC model 00:04:52
  10. Chapter 10 : Classification with Ensembles
    1. Ensembles in MATLAB 00:12:34
    2. Properties of Ensembles 00:05:28
  11. Chapter 11 : Validation Methods
    1. Cross validation options (Part 1) 00:10:07
    2. Cross validation options (Part 2) 00:10:08
  12. Chapter 12 : Performance Evaluation
    1. Making Predictions with the Models 00:08:06
    2. Determining the classification loss 00:07:59
    3. Classification Margins and Edge 00:15:23
    4. Classification Loss, Margins, Predictions and Edge for cross validated models 00:10:50
    5. Comparing two classifiers with holdout 00:13:16
    6. Computing Confusion Matrix 00:07:39
    7. Generating ROC Curve 00:09:46
    8. Generating ROC Curve based on the testing data 00:08:45
    9. More Customization and information while generating ROC 00:06:25
    10. Computing Accuracy, Error Rate, Specificity and Sensitivity (10) 00:05:11