h264, yuv420p, 1280×720 | English, aac, 44100 Hz, 2 channels, s16 | 12h 43mn | 5.8 GBInstructors: Israel GbatiPractical DSP in Python : Over 70 examples, FFT,Filter Design, IIR,FIR, Window Filters,Convolution,Linear Systems etcWhat you’ll learnDevelop the Convolution Kernel algorithm in PythonDesign and develop 17 different window filters in PythonDevelop the Discrete Fourier Transform (DFT) algorithm in PythonDesign and develop Type I Chebyshev filters in PythonDesign and develop Type II Chebyshev filters in PythonDevelop the Inverse Discrete Fourier Transform (IDFT) algorithm in PyhtonDevelop the Fast Fourier Transform (FFT) algorithm in PythonPerform spectral analysis on ECG signals in PythonDesign and develop Windowed-Sinc filters in PythonDesign and develop Finite Impulse Response (FIR) filters in PythonDesign and develop Infinite Impulse Response (IIR) filters in PythonDevelop the First Difference algorithm in PythonDevelop the Running Sum algorithm in PythonDevelop the Moving Average filter algorithm in PythonDevelop the Recursive Moving Average filter algorithm in PythonDesign and develop Butterworth filters in PythonDesign and develop Match filters in PythonDesign and develop Bessel filters in PythonSimulate Linear Time Invariant (LTI) Systems in PythonPerform linear and cubic interpolation in PythonRequirementsYou will need just a good working computer for this courseDescriptionWith a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Digital Signal Processing (DSP) in an engaging and easy to follow way.
The goal of this course is to present practical techniques while avoiding obstacles of abstract mathematical theories.
To achieve this goal, the DSP techniques are explained in plain language, not simply proven to be true through mathematical derivations.
Still keeping it simple, this course comes in different programming languages and hardware architectures so that students can put the techniques to practice using a programming language or hardware architecture of their choice.
This version of the course uses the Python programming language.
By the end of this course you should be able develop the Convolution Kernel algorithm in python, develop 17 different types of window filters in python, develop the Discrete Fourier Transform (DFT) algorithm in python, develop the Inverse Discrete Fourier Transform (IDFT) algorithm in pyhton, design and develop Finite Impulse Response (FIR) filters in python, design and develop Infinite Impulse Response (IIR) filters in python, develop Type I Chebyshev filters in python, develop Type II Chebyshev filters in python, perform spectral analysis on ECG signals in python, develop Butterworth filters in python, develop Match filters in python,simulate Linear Time Invariant (LTI) Systems in python, even give a lecture on DSP and so much more.
Please take a look at the full course curriculum.
Who this course is for:People working in the field of signal processingUniversity students taking classes in signal processingPython developers who wish to expand their skillsPeople who want to understand signal processing practically and apply it to their respective fields.
