English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6h 53m | 793 MB
Learn to Implement Classification Algorithms in One of the Most Power Tool used by Scientists and Engineer.
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
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
Table of Contents
Instructor and Course Introduction
Applications of Machine Learning
Course Outlines
Meet Your Instructor
Why use MATLAB for Machine Learning
MATLAB Crash Course
MATLAB GUI
MATLAB Pricing and Online Resources
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
Building a model with subset of classes, missing values and instances weights (6)
Dealing with scaling issue and copying a learned model (4)
Learning KNN model with features subset and with non-numeric data
Nearest Neighbor in MATLAB
Nearest Neighbor Intuition
Properties of KNN
Types of Properties (5)
Naive Bayes
A Final note on Naive Bayesian Model
Building a model with categorical data
Intuition of Naive Bayesian Classification
Naive Bayes in MATLAB
Decision Trees
Decision Trees in MATLAB
Intuition of Decision Trees
Node Related Properties of Decision Trees
Properties at the Classifier Built Time
Properties of the Decision Trees
Discriminant Analysis
Discriminant Analysis in MATLAB
Intuition of Discriminant Analysis
Properties of the Discriminant Analysis Learned Model in MATLAB
Support Vector Machines
Intuition of SVM Classification
Properties of SVM learned model in MATLAB
SVM in MATLAB
Error Correcting Output Codes
ECOC in MATLAB
ECOC name, value arguments
Intuition of ECOC
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
Classification Loss, Margins, Predictions and Edge for cross validated models
Classification Margins and Edge
Comparing two classifiers with holdout
Computing Accuracy, Error Rate, Specificity and Sensitivity (10)
Computing Confusion Matrix
Determining the classification loss
Generating ROC Curve based on the testing data
Generating ROC Curve
Making Predictions with the Models
More Customization and information while generating ROC
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