Machine Learning Classification Algorithms Using MATLAB at Simpliv 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.
Get more detailsVisit official programme website
- MATLAB crash course
- Grabbing and Importing a Dataset
- K-Nearest Neighbor
- Naive Bayes
- Decision Trees
- Discriminant Analysis
- 1 days
Start dates & application deadlines
DisciplinesComputer Sciences Human Computer Interaction Machine Learning View 649 other Short Courses in Computer Sciences in United States
We are not aware of any academic requirements for this programme.
We are not aware of any English requirements for this programme.
- Just basic high level math
International10 USD/fullTuition FeeBased on the tuition of 10 USD for the full programme during 1 days.
National10 USD/fullTuition FeeBased on the tuition of 10 USD for the full programme during 1 days.
Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
Apply and win up to €10000 to cover your tuition fees.
Updated in the last 3 months
Check the official programme website for potential updates.