Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes
Why you need to learn digital signal processing.
Nature is mysterious, beautiful, and complex. Trying to understand nature is deeply rewarding, but also deeply challenging. One of the big challenges in studying nature is data analysis. Nature likes to mix many sources of signals and many sources of noise into the same recordings, and this makes your job difficult.
Therefore, one of the most important goals of time series analysis and signal processing is to denoise: to separate the signals and noises that are mixed into the same data channels.
The big idea of DSP (digital signal processing) is to discover the mysteries that are hidden inside time series data, and this course will teach you the most commonly used discovery strategies.
What's special about this course?
The main focus of this course is on implementing signal processing techniques in MATLAB and in Python. Some theory and equations are shown, but I'm guessing you are reading this because you want to implement DSP techniques on real signals, not just brush up on abstract theory.
The course comes with over 10,000 lines of MATLAB and Python code, plus sample data sets, which you can use to learn from and to adapt to your own coursework or applications.
In this course, you will also learn how to simulate signals in order to test and learn more about your signal processing and analysis methods.
You will also learn how to work with noisy or corrupted signals.
Are there prerequisites?
You need some programming experience. I go through the videos in MATLAB, and you can also follow along using Octave (a free, cross-platform program that emulates MATLAB). I provide corresponding Python code if you prefer Python. You can use any other language, but you would need to do the translation yourself.
I recommend taking my Fourier Transform course before or alongside this course. However, this is not a requirement, and you can succeed in this course without taking the Fourier transform course.
What should you do now?
Watch the sample videos, and check out the reviews of my other courses -- many of them are "best-seller" or "top-rated" and have lots of positive reviews. If you are unsure whether this course is right for you, then feel free to send me a message. I hope you to see you in class!
Understand commonly used signal processing tools
Design, evaluate, and apply digital filters
Clean and denoise data
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I am a full-time educator and writer, and former professor of neuroscience. I "retired" from that position so I could focus my time and energy creating high-quality educational material just for you.I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors....
the instructor breaks down each topic to the fundamentals (no jargon) and helps me understand why the calculation is the way it is. This is very helpful.
I am finding this course very useful.
Mike is a very dedicated instructor, and his commitment to the subject exceeds the bare minimum to understand the course curriculum to pass exam etc.
The labs are easy to follow, and his teaching style is very intuitive. He also provided extra resources to ensure I got a broader understanding of the related subjects, for which I am very grateful for. Thanks Mike!
It would be great if you could create a course on using both continuous and discrete wavelets, at a slightly higher level (and, of course, with Python).
Best regards
good starting, up to now everything clear
Best course for DSP.
Awesome learning experience.
Nicely explained
Poor course
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Hossein M.
Great Mike