Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer
Then this course is for you If you are being facinated 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.
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
As bonus, you also learn how to share your analysis results with your collegues friends and others and create visual analysis of your results. You will also have access to some practice questions, which will give you hand on experience.
At the end of this course,
- You can confidently implement machine learning algorithms using MATLAB.
- You can perform meaningful analysis on the data.
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