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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 course consists of the following sections:
Segment 1: Instructor and Course Introduction
Segment 2: MATLAB Crash Course
Segment 3: Grabbing and Importing Data-set
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
As bonus, you also learn how to share your analysis results with your colleges, friends, and others, and create visual analysis of your results. You will also have access to some practice questions which will give you hands on experience.
03:04 Why MATLAB for machine learning
06:16 Meet the instructor, Dr. Nouman Azam
09:25 MATLAB crash course
19:30 Applications of machine learning
31:28 Data types you will encounter
39:55 Importing data into MATLAB
49:36 Data tables
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