Signal processing problems solved in MATLAB and in Python

Course Feature

Cost

Paid
Provider

Udemy
Certificate

Paid Certification
Language

English
Start Date

2023-07-01
Learners

No Information
Duration

No Information
Instructor

Mike X Cohen

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4.7

13,573 Ratings

Delve into the captivating world of digital signal processing (DSP) with the course "Signal Processing Problems Solved in MATLAB and in Python." Led by an experienced instructor, learners will unlock the secrets hidden within time series data through practical implementation of DSP techniques in MATLAB and Python. Gain valuable insights into denoising, simulating signals, and working with noisy data. With over 10,000 lines of code and sample datasets, this course offers a hands-on learning experience for students with some programming knowledge. Prepare to unravel the mysteries of nature and embark on a rewarding journey in signal processing.

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Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 24th, 2023]

The "Signal Processing Problems Solved in MATLAB and in Python" course provides a comprehensive exploration of digital signal processing (DSP) techniques. Aspiring learners, with a background in programming, will discover the fascinating world of time series data analysis and the art of denoising signals. The course prioritizes hands-on implementation in MATLAB and Python, providing practical code samples and data sets for experimentation and adaptation. Participants will also gain proficiency in simulating signals and handling noisy data. While prior knowledge of the Fourier transform is beneficial, it is not mandatory for success in this course. With engaging instruction and ample learning resources, students will delve into the complexities of DSP and its real-world applications.

Course Syllabus

Introductions

Time series denoising

Spectral and rhythmicity analyses

Working with complex numbers

Filtering

Convolution

Wavelet analysis

Resampling, interpolating, extrapolating

Outlier detection

Feature detection

Variability

Bonus section

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