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, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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!

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.


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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