If you want to learn one of the most hands-on tool for scientists and engineers, then MATLAB is a great tool to have on hand. From data analysts to scientist to even engineers, MATLAB offers a great numerical computing environment.
MATLAB, shortened from Matrix Laboratory, is a multi-paradigm numerical computing environment and fourth-generation programming language. It is commonly used for tasks such as matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, C#, Java, Fortran and Python.
You can even build your own apps within the software to help ease functions. Although, orignally designed for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing abilities.
This complex software is often a little difficult for people to master, and this is why we have designed this complete MATLAB guide that can help newbies, as well as intermediate learners to learn this tool from scratch. This course has been designed to help break down the more complex aspects of the software into simple and easy to understand short videos.
From understanding the user interface to actally designing your functions, you will learn it all in this amazing course.
What you will learn in this course:
Instructor and Course Introduction
Handling variables and Creating Scripts
Doing Basic Maths in MATLAB
Operations on Matrices
Advance Math Functions with Symbolic Data Type
Interacting with MATLAB and Graphics
Importing Data into MATLAB & File Handling and Text Processing
Sharing Your MATLAB Results
Cell Data Type, along with Tables and Time Tables
Working with Structures and Map Container Data Type
Converting between Different Data Types
All this and so much more awaits you. Enroll now and become a MATLAB master!
About the Instructor
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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