Best Signal Processing Courses

Find the best online Signal Processing Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Signal Processing Courses.

Signal processing problems, solved in MATLAB and in Python

Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes

Created by Mike X Cohen - Neuroscientist, writer, professor


Students: 8808, Price: $19.99

Students: 8808, Price:  Paid

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!

Complete neural signal processing and analysis: Zero to hero

Learn signal processing and statistics using brain electrical data with expert instruction and code challenges in MATLAB

Created by Mike X Cohen - Neuroscientist, writer, professor


Students: 3800, Price: $19.99

Students: 3800, Price:  Paid

Use your brain to learn signal processing, data analysis, and statistics... by learning about brains!

If you are reading this, I guess you have a brain. Your brain generates electrical signals that can be measured using electrodes, which are like small antennas. These electrical signals are rreeeeeaaallly complicated, because the brain is really complicated! 

But learning how to analyze brain electrical signals is an amazing and fascinating way to learn about signal processing, data visualization, spectral analysis, synchronization (connectivity) analyses, and statistics (in particular, permutation-based statistics).

What do you get in this course?

  • This course contains over 46 hours of video instruction, plus TONS of MATLAB exercises, problem sets, and challenges.

  • If you do all the MATLAB exercises, this course is easily well over 100 hours of educational content.

  • And you get access to the Q&A forum, where you can post specific questions about the course material and I answer as quickly as I can (typically 1-2 days).

  • By the end of this course, you will have confidence in processing, cleaning, analyzing, and performing statistics on brain electrical activity.

What do you need to know before joining this course?

I have tried to make this course accessible to anyone who is interested in learning neural signal processing and time series analysis.

I believe you can simply start this course without any formal background in neuroscience/biology, and without any background in signal processing/math/statistics. That said, some background in these topics will definitely be helpful.

However, I do assume that you have access to MATLAB (or Octave), and that you have some basic MATLAB coding skills (variables, for-loops, basic plotting). If you are a total noob to MATLAB, then please first take an intro-MATLAB course and then come back here.

Why should you trust this weird Mike X Cohen guy?

I've been teaching this material for almost 20 years. I'm really dedicated to teaching and I work really hard to improve my courses each year.

Check out the reviews of this course and my other courses to see what my students think of my teaching style and dedication.

I've also written several textbooks on neural data analysis and scientific programming. And there are more books and more courses on the way!

... but you have to watch out for my weird sense of humor. You've been warned...

PCA & multivariate signal processing, applied to neural data

Learn and apply cutting-edge data analysis techniques for the age of "big data" in neuroscience (theory and MATLAB code)

Created by Mike X Cohen - Neuroscientist, writer, professor


Students: 2801, Price: $19.99

Students: 2801, Price:  Paid

What is this course all about?

Neuroscience (brain science) is changing -- new brain-imaging technologies are allowing increasingly huge data sets, but analyzing the resulting Big Data is one of the biggest struggles in modern neuroscience (if don't believe me, ask a neuroscientist!).

The increases in the number of simultaneously recorded data channels allows new discoveries about spatiotemporal structure in the brain, but also presents new challenges for data analyses. Because data are stored in matrices, algorithms developed in linear algebra are extremely useful. 

The purpose of this course is to teach you some matrix-based data analysis methods in neural time series data, with a focus on multivariate dimensionality reduction and source-separation methods. This includes covariance matrices, principal components analysis (PCA), generalized eigendecomposition (even better than PCA!), and independent components analysis (ICA). The course is mathematically rigorous but is approachable to individuals with no formal mathematics background. MATLAB is the primary numerical processing engine but the material is easily portable to Python or any other language. 

You should take this course if you are a...

  • neuroscience researcher who is looking for ways to analyze your multivariate data.

  • student who wants to be competitive for a neuroscience PhD or postdoc position.

  • non-neuroscientist who is interested in learning more about the big questions in modern brain science.

  • independent learner who wants to advance your linear algebra knowledge.

  • mathematician, engineer, or physicist who is curious about applied matrix decompositions in neuroscience.

  • person who wants to learn more about principal components analysis (PCA) and/or independent components analysis (ICA)

  • intrigued by the image that starts off the Course Preview and want to know what it means! (The answers are in this course!)

Unsure if this course is right for you?

I worked hard to make this course accessible to anyone with at least minimal linear algebra and programming background. But this course is not right for everyone. Check out the preview videos and feel free to contact me if you have any questions.

I look forward to seeing you in the course!

Automotive Radar

FMCW Radar, Signal Processing, Applications in ADAS and Autonomous Driving

Created by Suchit Kr - Engineer


Students: 1140, Price: $89.99

Students: 1140, Price:  Paid

Autonomous Driving is getting its fame very fast across the world and lot of companies are investing huge money to reach the goal. As a result, there is high demand of Skilled people especially Engineers in this field. But as the field itself is quite complex and challenging, it demands multiple skills from one person.

Sensors are very important non-separable part in the Autonomous driving and knowledge of them is very important for everyone working in this field. Depending on the use, either basic knowledge or deep knowledge is necessary. Among the sensors, Camera, Radar, Lidar and Ultrasonic sensors are prominent for environment perception. There are lot of online resources available for Camera and computer vision, Lidar is still in development and even though Radar Technology is very mature in Automotive Sector and also having very wide scope of further research, very limited resources are available for beginners and for experience people at one location. Keeping this in mind, this course is created to provide basic and deep knowledge of Radar Technology with main focus in ADAS (Advanced Driver Assistance Systems) and AD (Autonomous Driving) applications.

This course will help to:

  • Understand ADAS and AD and the importance of various sensors in the field

  • How both ADAS and AD are connected to each other

  • Why Radar Technology is so important and how it works in this field

  • All about Automotive Radar - including Hardware components, basic and advance Signal processing and data processing

  • About FFT, Range, Doppler, Angle, RCS Measurements, RD map generation, Radar Detections, etc.

  • Briefly about Clustering, feature extractions, object formation, Single Object Tracking, Multi Object Tracking, etc.

Learn Digital Signal Processing – From Basics To Advance

Complete Course for 2021 - Digital Signal Processing, DSP, Signal Processing, DFT, FFT, Digital Filters

Created by Srinivas Andoor - Professional GATE ECE Faculty


Students: 87, Price: $89.99

Students: 87, Price:  Paid


***** Visit my website for better offers in Instructor profile *****|


DSP subject deals with Discrete time signal analysis, Discrete Time systems, DTFT, DFT, FFT, Digital Filters ( IIR and FIR filters). The course covers the essential elements of a DSP system from A/D conversion. The course starts with a detailed overview of discrete-time signals( periodic, even, odd, energy and power ) and systems, representation of the systems by means of differential equations, and their analysis using Fourier and z-transforms. Solving differential equations using Z-Transforms and finding frequency responses. Topics include sampling, impulse response, frequency response, finite and infinite impulse response systems, linear phase systems, digital filter design and implementation, discrete-time Fourier transforms, discrete Fourier transform, and the fast Fourier transform algorithms.

Understands the linear convolution( Graphical method and tabular form method) and circular convolution (Matrix and Concentric circle methods) and differences. This course deals with limitations of DTFT and DFT.FFT techniques are DIT-DFT, DIT-IDFT,DIF-DFT and DIF-IDFT techniques.

Digital filters are IIR and FIR filters, design methods and implementation.

Digital Signal Processing concludes with digital filter design and a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform.

This course covers Decimation( down sampling) and Interpolation ( up sampling)  operations.

This course covers multi rate signal processing and single rate signal processing.

I suggest you use the "Signals and Systems" book by Oppenheim or Digital Signal Processing By Proakis.

Signals And Systems – That Will Break Your Fear

You Can Master Advanced Subjects Like Control Systems, Communication Systems, Signal Processing & Robotics

Created by Amarender Reddy Byreddy - Senior GATE faculty


Students: 20, Price: $19.99

Students: 20, Price:  Paid








Signals and systems” is the basis of all control and signal processing engineering. It will allow you to take a real world machine, process (the system) and create a mathematical model, at which we apply stimuli and analyze it's response (stimuli and response being signals).

Examples of systems that manipulate signals are speech recognition, video streaming, cellular networks and medical scans such as MRI. The disciplines of signal and image processing are concerned with the analysis and synthesis of signals and their interaction with systems.

Students will

  • Be able to describe signals mathematically

  • Understand mathematical description and representation of continuous and discrete time signals

  • Be familiar with commonly used signals such as the unit step, ramp, impulse function, sinusoidal signals and complex exponential

  • Understand how to perform mathematical operations on signals

  • Be able to classify signals as continuous-time Vs. discrete-time, periodic Vs. non-periodic, energy signal Vs. power signal, odd Vs. even, causal Vs. non- causal signals

  • Understand system properties - linearity, time in variance, presence or absence of memory, causality, bounded-input bounded-output stability and invertibility

  • Be able to perform the process of convolution between signals and understand its implication for analysis of linear time-invariant systems. Understand the notion of an impulse response

  • Development of the mathematical skills to solve problems involving convolution

  • Understand and resolve the signals in frequency domain using Fourier series and Fourier transforms Further, be able to use the properties of the Fourier transform to compute the Fourier transform (and its inverse) for a broader class of signals

  • Understand the limitations of Fourier transform and need for Laplace transform and develop the ability to analyze the system in s- domain

  • Apply the Laplace transform and Z- transform for analyze of continuous-time and discrete-time signals and system