Engineers and Scientists understand the value of MATLAB, a shortened form of Matrix Laboratory and its importance in their data analysis. MATLAB is a multi-paradigm numerical computing environment and fourth-generation programming language.
It is commonly used for tasks such as analyzing data, developing algorithms, or creating models. It makes designing easier by simplifying many tasks and is built with interactive apps and tools. It also has the ability to scale analyses to run on clusters, GPUs, and clouds with only minor code changes, so you don’t require having to change major chunks of your code.
While there are multiple MATLAB beginner courses, we noticed there was a shortage of advanced MATLAB courses that covered details such as key data types such as cells, tables, time tables, structures and Map containers which should provide the students with the essential skills for taking full advantage of MATLAB strengths in data analysis and programming.
This is where this course steps in.
Our course has been designed to help cover advanced MATLAB functions, as well as discuss Types in detail and demonstrate different functions and operation and their conversions to make analysis and programming a greater experience.
Designed with an experienced instructor, the course is perfect for you to advance your MATLAB skills and boost your career!
What you’ll learn in this course:
Detailed introduction to the Course
Cell Data Types
Table Data Type
Time Table Data Type
Structures, including how to create them, retrieve data from a structure, other operations, etc.
How to create and Map Containers
Conversion between Different Data Types
So, what are you waiting for? Enroll now and let’s get started with MATLAB!
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.
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