Signal processing problems, solved in MATLAB and in Python from Udemy

Overview

Are you curious about unraveling the mysteries hidden inside time series data? Look no further! This digital signal processing (DSP) course is designed to teach you the most commonly used discovery strategies, with a focus on implementing these techniques in MATLAB and Python. And don’t worry if you’re not a fan of abstract theory – this course is all about getting down and dirty with real signals.

Coming with over 10,000 lines of code and sample data sets in both MATLAB and Python, this course puts you on the fast track to success. You’ll learn how to simulate signals, work with noisy or corrupted signals, and test your signal processing methods. While some programming experience is required, you can follow along in MATLAB or use the provided Python code. No need to be a Fourier Transform expert either – this course can work independently or alongside a Fourier Transform course. So why wait? Check out the sample videos and consider diving into the fascinating world of DSP.

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Skills you’ll learn

1. Implementing signal processing techniques in MATLAB and Python.
2. Simulating signals for testing and learning purposes.
3. Working with noisy or corrupted signals.
4. Understanding the basics of digital signal processing (DSP).
5. Denoising and separating signals in mixed data channels.
6. Discovering hidden patterns in time series data.
7. Applying DSP techniques to real-world signals.
8. Adapting provided code and sample datasets to your own projects.

Summary

Experience the joy of conquering signal processing challenges with the expert guidance of instructor Mike X Cohen in this spectacular course! With 99 enlightening lessons, you’ll join the ranks of over 13,000 enthusiastic students who have already benefited from this comprehensive treasure trove of knowledge. Embrace the power of MATLAB and Python as you embark on a journey that has earned a remarkable 4.7-star rating from participants, who can attest to its sheer brilliance!