Machine Learning for Data Science using MATLAB
4.5 (182 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
915 students enrolled

Machine Learning for Data Science using MATLAB

Learn to implement classification and clustering algorithms using MATLAB with practical examples, projects and datasets
4.5 (182 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
915 students enrolled
Created by Nouman Azam
Last updated 9/2019
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 9.5 hours on-demand video
  • 6 articles
  • 6 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • How to implement different machine learning classification algorithms using matlab.
  • How to impplement different machine learning clustering algorithms using matlab.
  • How to proprocess data before analysis.
  • When and how to use dimensionality reduction.
  • Take away code templates.
  • Visualization results of algorithms
  • Decide which algorithm to choose for your dataset
Course content
Expand all 64 lectures 09:25:22
+ Introduction to course and MATLAB
3 lectures 16:29
MATLAB essentials for the course
Tell us about the course
+ --------------------------- Data Preprocessing ---------------------------
11 lectures 01:08:57
Code and Data
Section Introduction
Removing Missing Data (Part 2)
Feature Scaling
Handling Outliers (Part 1)
Handling Outliers (Part 2)
Dealing with Categorical Data (Part 1)
Dealing with categorical data (Part 2)
Your Preprocessing Template
+ K-Nearest Neighbor
8 lectures 01:15:29
KNN Intuition
KNN in MATLAB (Part 1)
KNN in MATLAB (Part 2)
Visualizing the Decision Boundaries of KNN
Explaining the code for visualization
Here is our classification template
How to change default options and customize classifiers
Customization options for KNN
+ Naive Bayes
4 lectures 36:48
Naive Bayesain in MATLAB
Customization Options for Naive Bayesain
+ Decision Trees
4 lectures 33:34
Decision trees intuition
Decision Trees in MATLAB
Visualizing Decision Trees using the View Function
Customization Options for Decision Trees
+ Support Vector Machines
4 lectures 38:13
Kernel SVM Intuition
Customization Options for SVM
+ Discriminant Analysis
3 lectures 22:16
Discriminant Analysis Intuition
Discriminant Analysis in MATLAB
Customization Options for Discriminant Analysis
+ Ensembles
3 lectures 36:10
Ensembles Intuition
Ensembles in MATLAB
+ Performance Evaluation
5 lectures 56:57
Evaluating Classifiers: Confusion matrix (Theory)
Validation Methods (Theory)
Validation methods in MATLAB (Part 1)
Validation methods in MATLAB (Part 2)
Evaluating Classifiers in MATLAB
  • MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required
  • In version below 2017a there might be some functions that will not work

Basic Course Description 

This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or figure out how to implement and solve data science problems with it. 

The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide. 

Below is the brief outline of this course. 

Segment 1: Introduction to course

Segment 2: Data preprocessing 

Segment 3: Classification Algorithms in MATLAB

Segment 4: Clustering Algorithms in MATLAB

Segment 5: Dimensionality Reduction

Segment 6: Project: Malware Analysis


Your Benefits and Advantages: 

  • If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!

  • You will be sure of receiving quality contents since the instructors has already many courses in the MATLAB on udemy. 

  • 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...

We are excited to see you in the course!

Best Regrads,

Dr. Nouman Azam


More Benefits and Advantages: 

✔ You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses on Udemy in the MATLAB niche. 

✔ The titles of these courses are 

  • Complete MATLAB Tutorial: Go from Beginner to Pro

  • MATLAB App Desigining: The Ultimate Guide for MATLAB Apps

  • Machine Learning Classification Algorithms using MATLAB

  • Create Apps in MATLAB with App Designer (Codes Included)

  • Advance MATLAB Data Types and Data Structures


Student Testimonials for Dr. Nouman Azam!


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


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)"

Who this course is for:
  • Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
  • Anyone who want to analyze the data