The Essential MATLAB & Simulink Certification Training Bundle

The Essential MATLAB & Simulink Certification Training Bundle

5 Reviews
1461 Enrolled
25 Hours
$48.99 CAD$90.00 CAD
46% Off
Machine Learning Classification Algorithms Using MATLAB

50 Lessons (6.5h)

  • Course and Instructor Introduction
  • MATLAB Crash Course
  • Grabbing and Importing a Dataset
  • K-Nearest Neighbor
  • Naive Bayes
  • Decision Trees
  • Discriminant Analysis
  • Support Vector Machines
  • Error Correcting Output Codes
  • Classification with Ensembles
  • Validation Methods
  • Performance Evaluation
DescriptionInstructorImportant DetailsReviewsRelated Products

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

NA
Nouman Azam

Instructor

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.

Description

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

Specs

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

Requirements

  • Students must install MATLAB on their computers

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
5 Reviews
4.6/ 5
All reviews are from verified purchasers collected after purchase.
ak

ali khalife

Verified Buyer

It contains great courses which helped me improve significantly in MATLAB. I recommend it to those who want to elevate their skills.

Jul 29, 2020
Enrico Cimador profile picture

Enrico Cimador

Verified Buyer

Dec 20, 2018
GB

Gustavo Barboaa

Verified Buyer

Nov 18, 2018
Andrea Zuccotto profile picture

Andrea Zuccotto

Verified Buyer

I really think I never spent better my money: > Incredible course offer > Top Level didactics > Worth every penny

Nov 10, 2018
VS

Vishweshwer Shastri

Verified Buyer

The hyperlink to the course certificates are not accessible. The course certificates are neither downloadable nor shareable. While the course content is satisfactory and recommendable for those who want to delve in MATLAB for research works.

Oct 18, 2018
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