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

Basic Course 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 Ouput 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 esesential ideas. The following are the course outlines.

Sgement 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 Ouput Codes 

Segment 10: Classification with Ensembles  

Segment 11: Validation Methods

Segment 12: Evaluating Performance

At the end of this course, 

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