Curriculum
12 Sections
56 Lessons
52 Weeks
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Instructor and Course Introduction
4
1.1
Applications of Machine Learning
1 Minute
1.2
Why use MATLAB for Machine Learning
4 Minutes
1.3
Meet Your Instructor
2 Minutes
1.4
Course Outlines
2 Minutes
MATLAB Crash Course
3
2.1
MATLAB Pricing and Online Resources
5 Minutes
2.2
MATLAB GUI
5 Minutes
2.3
Some common Operations
12 Minutes
Grabbing and Importing a Dataset
4
3.1
Data Types that We May Encounter
6 Minutes
3.2
Grabbing a dataset
2 Minutes
3.3
Importing Data into MATLAB
10 Minutes
3.4
Understanding the Table Data Type
11 Minutes
K-Nearest Neighbor
7
4.1
Nearest Neighbor Intuition
9 Minutes
4.2
Nearest Neighbor in MATLAB
10 Minutes
4.3
Learning KNN model with features subset and with non-numeric data
11 Minutes
4.4
Dealing with scalling issue and copying a learned model (4)
3 Minutes
4.5
Types of Properties (5)
11 Minutes
4.6
Building a model with subset of classes, missing values and instances weights (6)
7 Minutes
4.7
Properties of KNN
5 Minutes
Naive Bayes
5
5.1
Intuition of Naive Bayesain Classification
16 Minutes
5.2
Naive Bayes in MATLAB
10 Minutes
5.3
Building a model with categorical data
6 Minutes
5.4
A Final note on Naive Bayesain Model
3 Minutes
5.5
Notes and Practice
30 Minutes
Decision Trees
6
6.1
Intuition of Decision Trees
9 Minutes
6.2
Decision Trees in MATLAB
5 Minutes
6.3
Properties of the Decision Trees
15 Minutes
6.4
Node Related Properties of Decision Trees
9 Minutes
6.5
Properties at the Classifer Built Time
8 Minutes
6.6
Notes and Practice
30 Minutes
Discriminant Analysis
4
7.1
Intuition of Discriminant Analysis
7 Minutes
7.2
Discriminant Analysis in MATLAB
4 Minutes
7.3
Properties of the Discriminant Analysis Learned Model in MATLAB
7 Minutes
7.4
Notes and Practice
30 Minutes
Support Vector Machines
4
8.1
Intuition of SVM Classification
8 Minutes
8.2
SVM in MATLAB
13 Minutes
8.3
Properties of SVM learned model in MATLAB
13 Minutes
8.4
Notes and Practice
30 Minutes
Error Correcting Output Codes
5
9.1
Intuition of ECOC
6 Minutes
9.2
ECOC in Matlab
9 Minutes
9.3
ECOC name, value arguemnts
13 Minutes
9.4
Properties of ECOC model
5 Minutes
9.5
Notes and Practice
30 Minutes
Classification with Ensembles
2
10.1
Ensembles in MATLAB
12 Minutes
10.2
Properties of Ensembles
6 Minutes
Validation Methods
2
11.1
Cross validation options (Part 1)
10 Minutes
11.2
Cross validation options (Part 2)
10 Minutes
Performance Evaluation
10
12.1
Making Predictions with the Models
8 Minutes
12.2
Determining the classification loss
8 Minutes
12.3
Classification Margins and Edge
15 Minutes
12.4
Classification Loss, Margins, Predictions and Edge for cross validated models
11 Minutes
12.5
Comparing two classifiers with holdout
13 Minutes
12.6
Computing Confusion Matrix
8 Minutes
12.7
Generating ROC Curve
10 Minutes
12.8
Generating ROC Curve based on the testing data
9 Minutes
12.9
More Customization and information while generating ROC
6 Minutes
12.10
Computing Accuracy, Error Rate, Specificity and Sensitivity (10)
6 Minutes
Machine Learning- Classification of Algorithms using MATLAB
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