Signal processing problems solved in MATLAB and in Python
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 Feature
Cost:
Paid
Provider:
Udemy
Certificate:
Paid Certification
Language:
English
Start Date:
2023-07-01
Course Overview
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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
Course Provider
Provider Udemy's Stats at AZClass
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