Course: Machine Learning Classification Algorithms using MATLAB

Machine Learning Classification Algorithms using MATLAB

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

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.

  • Segment 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

Student Testimonials!

This is the second Simpliv class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

This course is really good for a beginner. It will help you to start from ground up and move on to more complicated areas. Though it does not cover Matlab toolboxes etc, it is still a great basic introduction for the platform. I do recommend getting yourself enrolled for this course.Excellent course and instructor. You learn all the fundamentals of using MATLAB.

Lakmal Weerasinghe

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

"Concepts are explained very well, Keep it up Sir...!!!"

Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"

Your Benefits and Advantages:

  • You receive knowledge from a PhD. in Computer science (machine learning) with over 10 years of teaching and research experience, In addition to 15 years of programming experience and another decade of experience in using MATLAB
  • The instructor has 6 courses on Simpliv on MATLAB including a best seller course. 
  • The overall rating in these courses are (4.5/5)
  • If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
  • You have lifetime access to the course
  • You have instant and free access to any updates i add to the course
  • You have access to all Questions and discussions initiated by other students
  • You will receive my support regarding any issues related to the course

Check out the curriculum and Freely available lectures for a quick insight.

It's time to take Action!

Click the "Take This Course" button at the top right now!

Time is limited and Every second of every day is valuable.

I am excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

Who is the target audience?

  • Researchers, Entrepreneurs, Instructors and Teachers, College Students, Engineers, Programmers and Simulators
Basic knowledge
  • Just basic high level math
What you will learn
  • Use machines learning algorithms confidently in MALTAB
  • Build classification learning models and customize them based on the datasets
  • Compare the performance of diffferent classification algorithms
  • Learn the intuition behind classification algorithms
  • Create automatically generated reports for sharing your analysis results with friends and colleague
Number of Lectures: 51
Total Duration: 06:53:31
Instructor and Course Introduction
  • Applications of Machine Learning  
  • Why use MATLAB for Machine Learning  
  • Meet Your Instructor  
  • Course Outlines  
MATLAB Crash Course
  • MATLAB Pricing and Online Resources  
  • Some common Operations  
Grabbing and Importing a Dataset
  • Data Types that We May Encounter  
  • Grabbing a dataset  
  • Importing Data into MATLAB  
  • Understanding the Table Data Type  
K-Nearest Neighbor
  • Nearest Neighbor Intuition  
  • Nearest Neighbor in MATLAB  
  • Learning KNN model with features subset and with non-numeric data  
  • Dealing with scalling issue and copying a learned model (4)  
  • Types of Properties (5)  
  • Building a model with subset of classes, missing values and instances weights  
  • Properties of KNN  
Naive Bayes
  • Intuition of Naive Bayesain Classification  
  • Naive Bayes in MATLAB  
  • Building a model with categorical data  
  • A Final note on Naive Bayesain Model  
Decision Trees
  • Intuition of Decision Trees  
  • Decision Trees in MATLAB  
  • Properties of the Decision Trees  
  • Node Related Properties of Decision Trees  
  • Properties at the Classifier Built Time  
Discriminant Analysis
  • Intuition of Discriminant Analysis  
  • Discriminant Analysis in MATLAB  
  • Properties of the Discriminant Analysis Learned Model in MATLAB  
Support Vector Machines
  • Intuition of SVM Classification  
  • SVM in MATLAB  
  • Properties of SVM learned model in MATLAB  
Error Correcting Output Codes
  • Intuition of ECOC  
  • ECOC in Matlab  
  • ECOC name, value arguments  
  • Properties of ECOC model  
Classification with Ensembles
  • Ensembles in MATLAB  
  • Properties of Ensembles  
Validation Methods
  • Cross validition options (Part 1)  
  • Cross validition options (Part 2)  
Performance Evaluation
  • Making Predictions with the Models  
  • Determining the classification loss  
  • Classification Margins and Edge  
  • Classification Loss, Margins, Predictions and Edge for cross validated models  
  • Comparing two classifiers with holdout  
  • Computing Confusion Matrix  
  • Generating ROC Curve  
  • Generating ROC Curve based on the testing data  
  • More Customization and information while generating ROC  
  • Computing Accuracy, Error Rate, Specificity and Sensitivity (10)  
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