The Essential MATLAB & Simulink Certification Training Bundle

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25 Hours
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50 Lessons (6.5h)

  • Course and Instructor Introduction
    Applications of Machine Learning1:35
    Why use MATLAB for Machine Learning3:13
    Meet Your Instructor1:24
    Course Outlines1:43
  • MATLAB Crash Course
    MATLAB Pricing and Online Resources
    MATLAB GUI4:57
    Some common Operations11:56
  • Grabbing and Importing a Dataset
    Data Types that We May Encounter6:02
    Grabbing a dataset2:20
    Importing Data into MATLAB9:35
    Understanding the Table Data Type11:36
  • K-Nearest Neighbor
    Nearest Neighbor Intuition9:19
    Nearest Neighbor in MATLAB9:39
    Learning KNN model with features subset and with non-numeric data10:48
    Dealing with scalling issue and copying a learned model3:32
    Types of Properties11:22
    Building a model with subset of classes, missing values and instances weights6:58
    Properties of KNN5:08
  • Naive Bayes
    Intuition of Naive Bayesain Classification15:43
    Naive Bayes in MATLAB10:34
    Building a model with categorical data6:24
    A Final note on Naive Bayesain Model3:00
  • Decision Trees
    Intuition of Decision Trees9:01
    Decision Trees in MATLAB5:35
    Properties of the Decision Trees14:24
    Node Related Properties of Decision Trees9:20
    Properties at the Classifer Built Time7:25
  • Discriminant Analysis
    Intuition of Discriminant Analysis6:44
    Discriminant Analysis in MATLAB4:41
    Properties of the Discriminant Analysis Learned Model in MATLAB7:03
  • Support Vector Machines
    Intuition of SVM Classification7:41
    SVM in MATLAB12:34
    Properties of SVM learned model in MATLAB12:46
  • Error Correcting Output Codes
    Intuition of ECOC5:29
    ECOC in Matlab9:15
    ECOC name, value arguemnts12:59
    Properties of ECOC model4:51
  • Classification with Ensembles
    Ensembles in MATLAB12:33
    Properties of Ensembles5:28
  • Validation Methods
    Cross validition options (Part 1)10:07
    Cross validition options (Part 2)10:08
  • Performance Evaluation
    Making Predictions with the Models8:06
    Determining the classification loss7:59
    Classification Margins and Edge15:23
    Classification Loss, Margins, Predictions and Edge for cross validated models10:49
    Comparing two classifiers with holdout13:16
    Computing Confusion Matrix7:38
    Generating ROC Curve9:45
    Generating ROC Curve based on the testing data8:45
    More Customization and information while generating ROC6:25
    Computing Accuracy, Error Rate, Specificity and Sensitivity5:10

Demystify Machine Learning & Create Stunning Data Models with 180+ MATLAB & Simulink Lessons

Nouman Azam


Nouman Azam received his Ph.D. Degree in Computer Sceince from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from the National University of Sciences and Technology, Pakistan and earned his Bachelors in Computer Sciences from the National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005 respectively

He is the creator of six online MATLAB courses. He has extensive knowledge of tools, such as MATLAB, QTSpim, C++, Java, LaTeX and other academic resources used for teaching and instructing purposes. Overall, he has over 10 years of teaching and relevant experience at undergraduate and graduate level.


As the name suggests, classification algorithms are what allow computers to well...classify new observations, like how your inbox decides which incoming emails are spam or how Siri recognizes your voice. This course will show you how to implement classification algorithms using MATLAB, one of the most powerful tools inside a data scientist's toolbox. Following along step-by-step, you'll start with the MATLAB basics then move on to working with key classification algorithms, like K-Nearest Neighbor, Discriminant Analysis, and more as you come to grips with this machine learning essential. Upon completion of this course, and all courses included in the bundle, you'll also receive a certification of completion validating your new skills! This is especially useful for including in your portfolio or resume, so future employers can feel confident in your skill set.

  • Access 50 lectures & 6.5 hours of content 24/7
  • Explore the MATLAB basics & the Statistic and Machine Learning toolbox
  • Familiarize yourself w/ key classification algorithms, like K-Nearest Neighbor & Decision Trees
  • Learn how to confidently implement machine learning algorithms using MATLAB
  • Understand how to perform a meaningful analysis of your data & share it w/ others


Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop and mobile
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner


  • Students must install MATLAB on their computers


  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.