Classification Loss Margins Predictions and Edge for cross validated models

Description
This course is for you If you are being fascinated by the field of Machine Learning?

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.
Courses:
Instructor and Course Introduction
MATLAB Crash Course
Grabbing and Importing a Dataset
K-Nearest Neighbor
Naive Bayes
Decision Trees
Discriminant Analysis
Support Vector Machines
Error Correcting Output Codes
Classification with Ensembles

Applications of Machine Learning,Why use MATLAB for Machine learning,matlab gui,importing data into matlab,generating ROC Curve,Generating ROC Curve based on the testing data,Matlab Crash course,grabbing and importing a dataset,K-Nearest Neighbor,Naive Bayes,Decision Trees,Discriminant Analysis,Support Vector Machines,Error Correcting Output Codes,Classification with Ensembles,