MATLAB for Students, Engineers, and Professionals in STEM

Complete guide to MATLAB for beginners and experts

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Course Data

Lectures: 31
Length: 4h 12m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

MATLAB is a programming language you want to know if you're in academia or working in engineering and science. It's used in all kinds of scientific disciplines and applications including:

  • mathematics (duh)
  • aerospace engineering
  • image and sound signal processing
  • physics simulations
  • partial differential equations (PDEs)
  • structural mechanics
  • electrostatics and magnetostatics
  • AC Power electromagnetics
  • DC conduction
  • eigenvalue problems
  • control systems engineering
  • computational biology
  • deep learning
  • machine learning
  • data science
  • data analytics
  • artificial intelligence
  • embedded systems
  • FPGA design
  • computer vision
  • Internet of Things (IoT)
  • robotics
  • wireless communications
  • biotechnology
  • neuroscience
  • quantitative finance and risk management
  • computational finance
  • earth, ocean, and atmospheric sciences
  • materials science
  • semiconductor engineering
  • statistics
  • optimization
  • parallel computing

The possibilities really are endless. When I say it's used for pretty much everything, I really mean everything.

Unlike Python, it is a language that is optimized for math and science computation. In other words: it's fast and efficient.

This course contains 3 major components.

1) The first section focuses on the basics of the language: syntax, basic operations, basic programming logic, and plotting.

2) The second section focuses on a specific application: signal processing. I chose signal processing because it deals with a very intuitive type of data (images and sound) which most people are readily able to understand.

In addition, the methods we use on these "signals" can be applied to any field: whether it be quantitative finance, electromagnetics, time series analysis, or deep learning. So, its applicability in the real-world is wide-ranging.

3) The third section focuses on probability and statistics, which is essential in many modern STEM fields such as finance, engineering, and machine learning. As with signal processing, its use in the real-world is ubiquitous.

It's important to remember that MATLAB is not for everyone. Unlike Python, which is a language that I use daily for a wide variety of tasks both inside and outside math and science, MATLAB is not free. Thus, usually those in academia (like students and professors) or those who went into industry from academia (such as financial engineers, research scientists, etc.) are the most likely candidates for learning MATLAB. Usually you will learn MATLAB because everyone around you is using MATLAB; your circumstances require it.

Thus, this course is for those of you who want to learn the basic fundamentals of MATLAB, so you can become better at whatever you are doing (a project at your job or a class at your university).

Instructor's note: This is the very first course I ever made which is no longer available anywhere else. You can think of it like a "lost course", for those of you who are interested in having a full collection of my content. =) It's certainly not as polished as my newest courses, and it is terse and to the point. If you like that style, then this course is for you!


Intro and Logistics

1 Lectures · 03min

Part 1: The Basics

10 Lectures · 01hr 45min
  1. What is MATLAB? How to get MATLAB (07:14)
  2. Basic Syntax (10:42)
  3. Matrix Arithmetic (08:59)
  4. Functions (14:50)
  5. Linear Algebra (11:16)
  6. Loops and Conditionals (15:46)
  7. Data Types and Data Structures (11:14)
  8. Plotting (Part 1) (08:51)
  9. Plotting (Part 2) (08:08)
  10. Loading and Saving Data (08:00)

Part 2: Signal Processing

10 Lectures · 01hr 21min
  1. Sound (part 1) (09:42)
  2. Sound (part 2) (07:25)
  3. Signal Processing (Part 1) (14:33)
  4. Signal Processing (Part 2) (07:55)
  5. Low Pass Filter (08:45)
  6. Images (06:27)
  7. Manipulating Images (07:18)
  8. Convolution (05:27)
  9. Gaussian Filter (06:06)
  10. Blur and Edge Detection (07:49)

Part 3: Probability and Statistics

10 Lectures · 01hr 01min
  1. Probability Outline (03:12)
  2. What is Probability? (04:04)
  3. Measuring Probability (07:10)
  4. Generating Random Values (08:54)
  5. Birthday Paradox (07:51)
  6. Continuous Variables (04:42)
  7. Mean and Variance (04:42)
  8. The Gaussian Distribution (08:08)
  9. Tests for Normality (07:17)
  10. Multivariate Gaussian (05:49)


  • Files for Part 2
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