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:

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!

- 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

Unlike Python, it is a language that is

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

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

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.

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.

READ MORE

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

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. :)

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. ...

READ MORE

- What is MATLAB? How to get MATLAB (07:14)
- Basic Syntax (10:42)
- Matrix Arithmetic (08:59)
- Functions (14:50)
- Linear Algebra (11:16)
- Loops and Conditionals (15:46)
- Data Types and Data Structures (11:14)
- Plotting (Part 1) (08:51)
- Plotting (Part 2) (08:08)
- Loading and Saving Data (08:00)

- Sound (part 1) (09:42)
- Sound (part 2) (07:25)
- Signal Processing (Part 1) (14:33)
- Signal Processing (Part 2) (07:55)
- Low Pass Filter (08:45)
- Images (06:27)
- Manipulating Images (07:18)
- Convolution (05:27)
- Gaussian Filter (06:06)
- Blur and Edge Detection (07:49)

- Probability Outline (03:12)
- What is Probability? (04:04)
- Measuring Probability (07:10)
- Generating Random Values (08:54)
- Birthday Paradox (07:51)
- Continuous Variables (04:42)
- Mean and Variance (04:42)
- The Gaussian Distribution (08:08)
- Tests for Normality (07:17)
- Multivariate Gaussian (05:49)

- Files for Part 2