Machine Learning Classification Algorithms using MATLAB
4.1 (11 ratings)
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Machine Learning Classification Algorithms using MATLAB

Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer
4.1 (11 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,644 students enrolled
Created by Nouman Azam
Last updated 8/2017
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
  • 7 hours on-demand video
  • 8 Articles
  • 9 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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 anlaysis results with friends and collegue
View Curriculum
  • Just basic high level maths

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. 


Student Testimonials!


This is the second Udemy 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 Ph.D. in Computer science (machine learning) with over 10 years of teaching and reaserch experience, In addition to 15 years of programming experience and another decade of experience in using MATLAB.
  • The instructor has 6 courses on udemy 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
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Curriculum For This Course
59 Lectures
MATLAB Crash Course
4 Lectures 21:55
Grabbing and Importing a Dataset
5 Lectures 29:35

Importing Data into MATLAB

Understanding the Table Data Type

Notes and Practice
K-Nearest Neighbor
8 Lectures 56:48
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 (6

Properties of KNN

Notes and Practice
Naive Bayes
5 Lectures 36:03

Naive Bayes in MATLAB

Building a model with categorical data

A Final note on Naive Bayesain Model

Notes and Practice
Decision Trees
6 Lectures 45:47
Intuition of Decision Trees

Decision Trees in MATLAB

Properties of the Decision Trees

Node Related Properties of Decision Trees

Properties at the Classifer Built Time

Notes and Practice
Discriminant Analysis
4 Lectures 18:30
Intuition of Discriminant Analysis

Discriminant Analysis in MATLAB

Properties of the Discriminant Analysis Learned Model in MATLAB

Notes and Practice
Support Vector Machines
4 Lectures 33:03
Intuition of SVM Classification


Properties of SVM learned model in MATLAB

Notes and Practice
Error Correcting Output Codes
5 Lectures 32:36
Intuition of ECOC

ECOC in Matlab

ECOC name, value arguemnts

Properties of ECOC model

Notes and Practice
Classification with Ensembles
2 Lectures 18:01
Ensembles in MATLAB

Properties of Ensembles
2 More Sections
About the Instructor
Nouman Azam
4.4 Average rating
161 Reviews
4,914 Students
7 Courses
Your Computer Science Professor

I am Dr. Nouman Azam and i am Assistant Professor at the Department of Computer Science, National University of Computer and Emerging Sciences.I had over a decade of experience in research and teaching. 

My research work is mainly on the exploration of machine learning techniques in application areas such as bioinformatics, text summarization, text categorization, email filtering, security, recommender systems and medical decision making. On theoretical side, i am interested in applying the theories of rough sets, game theory, optimization and conflict analysis to machine learning tasks. For my Masters thesis, i applied different machine learning techniques to select important features in spam email filtering. For my doctoral thesis, i investigated the applications of machine learning techniques such as rough sets, game theory, genetic algorithms, gradient descent and others to learn and extract data patterns. In the recent past, I taught many machine learning related courses at the undergraduate and graduate levels. 

MATLAB remained my number one choice for implementing ideas and converting my code to meaningful softwares. I implemented all the code of my Masters and Doctoral thesis in MATLAB. Currenlty, i have six courses on MATLAB on the udemy plateform including best seller and top ranked courses.