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Python for Signal and Image Processing Master Class

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Python for Signal and Image Processing Master Class, available at $94.99, has an average rating of 4.2, with 183 lectures, based on 61 reviews, and has 534 subscribers.

You will learn about Fundamentals of Signals and Image Processing. Analog to digital conversion. Sampling and Reconstruction. Nyquist Theorem. Convolution for Signal and Images. Signal and Image denoising. Fourier transform of Signals and Images. Signal filtering by FIR and IIR filters. Image Filtering in Spatial and Frequency Domain Wavelet Transform for Signal and Images. Histogram Processing Arithmetic, Logic and Point Level Operations on Images Implementation of all Signal and Image Processing Algorithms in Python Python Crash Course This course is ideal for individuals who are Anyone who wants to learn Signal and Image Processing from scratch using Python. or Anyone who wants to work in Signal and Image Processing area. or Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python. or Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques. or Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques. or Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python. It is particularly useful for Anyone who wants to learn Signal and Image Processing from scratch using Python. or Anyone who wants to work in Signal and Image Processing area. or Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python. or Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques. or Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques. or Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python.

Enroll now: Python for Signal and Image Processing Master Class

Summary

Title: Python for Signal and Image Processing Master Class

Price: $94.99

Average Rating: 4.2

Number of Lectures: 183

Number of Published Lectures: 183

Number of Curriculum Items: 183

Number of Published Curriculum Objects: 183

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Fundamentals of Signals and Image Processing.
  • Analog to digital conversion.
  • Sampling and Reconstruction.
  • Nyquist Theorem.
  • Convolution for Signal and Images.
  • Signal and Image denoising.
  • Fourier transform of Signals and Images.
  • Signal filtering by FIR and IIR filters.
  • Image Filtering in Spatial and Frequency Domain
  • Wavelet Transform for Signal and Images.
  • Histogram Processing
  • Arithmetic, Logic and Point Level Operations on Images
  • Implementation of all Signal and Image Processing Algorithms in Python
  • Python Crash Course

Who Should Attend

  • Anyone who wants to learn Signal and Image Processing from scratch using Python.
  • Anyone who wants to work in Signal and Image Processing area.
  • Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python.
  • Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
  • Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
  • Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python.

Target Audiences

  • Anyone who wants to learn Signal and Image Processing from scratch using Python.
  • Anyone who wants to work in Signal and Image Processing area.
  • Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python.
  • Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
  • Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
  • Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python.

This course will bridge the gap between the theory and implementation of Signal and Image Processing Algorithms and their implementation in Python. All the lecture slides and python codes are provided.

Why Signal Processing?

Since the availability of digital computers in the 1970s, digital signal processing has found its way in all sections of engineering and sciences.

Signal processing is the manipulation of the basic nature of a signal to get the desired shaping of the signal at the output. It is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals.

Following areas of sciences and engineering are specially benefitted by rapid growth and advancement in signal processing techniques.

1. Machine Learning.

2. Data Analysis.

3. Computer Vision.

4. Image Processing

5. Communication Systems.

6. Power Electronics.

7. Probability and Statistics.

8. Time Series Analysis.

9. Finance

10. Decision Theory

Why Image Processing?

Image Processing has found its applications in numerous fields of Engineering and Sciences.

Few of them are the following.

1. Deep Learning

2. Computer Vision

3. Medical Imaging

4. Radar Engineering

5. Robotics

6. Computer Graphics

7. Face detection

8. Remote Sensing

9. Agriculture and food industry

Course Outline

Section 01: Introduction of the course

Section 02: Python crash course

Section 03: Fundamentals of Signal Processing

Section 04: Convolution

Section 05: Signal Denoising

Section 06: Complex Numbers

Section 07: Fourier Transform

Section 08: FIR Filter Design

Section 09: IIR Filter Design

Section 10: Introduction to Google Colab

Section 11: Wavelet Transform of a Signal

Section 12: Fundamentals of Image Processing

Section 13: Fundamentals of Image Processing With NumPy and Matplotlib

Section 14: Fundamentals of Image Processing with OpenCV

Section 15: Arithmetic and Logic Operations with Images

Section 16: Geometric Operations with Images

Section 17: Point Level OR Gray level Transformation

Section 18: Histogram Processing

Section 19: Spatial Domain Filtering

Section 20: Frequency Domain Filtering

Section 21: Morphological Processing

Section 22: Wavelet Transform of Images

Course Curriculum

Chapter 1: Introduction of the Course

Lecture 1: Introduction of the Course

Lecture 2: Pace of the Lecture Delivery

Lecture 3: Course Material

Chapter 2: Python Crash Course

Lecture 1: Introduction of the Section

Lecture 2: Python Installment

Lecture 3: Installing Python Packages

Lecture 4: Introduction of Jupyter Notebook

Lecture 5: Arithmetic Operations Part01

Lecture 6: Arithmetic Operations Part02

Lecture 7: Arithmetic Operations Part03

Lecture 8: Dealing With Arrays Part01

Lecture 9: Dealing With Arrays Part02

Lecture 10: Dealing With Arrays Part03

Lecture 11: Plotting and Visualization Part01

Lecture 12: Plotting and Visualization Part02

Lecture 13: Plotting and Visualization Part03

Lecture 14: Plotting and Visualization Part04

Lecture 15: Lists in Python

Lecture 16: For Loop Part01

Lecture 17: For Loop Part02

Chapter 3: Fundamentals of Signal Processing

Lecture 1: Introduction of the Section

Lecture 2: Basic Elements of Signal Processing

Lecture 3: AD Conversion

Lecture 4: AD Conversion With Python

Lecture 5: Coding the Quantized Signal

Lecture 6: Fundamentals of Continuous time signals

Lecture 7: Continuous time signals in Python

Lecture 8: Fundamentals of Discrete time signals

Lecture 9: Discrete time signals in python

Lecture 10: Sampling and Reconstruction

Lecture 11: Sampling and Reconstruction in Python

Chapter 4: The Convolution

Lecture 1: Introduction of the Section

Lecture 2: The Convolution Sum

Lecture 3: Numerical Example on Convolution

Lecture 4: Full mode convolution

Lecture 5: Convolution Using For Loop in Python

Lecture 6: Convolution Using Numpy

Lecture 7: Signal Denoising by Convolution

Lecture 8: Edge Detection by Convolution

Lecture 9: The Convolution Theorem

Chapter 5: Signal Denoising

Lecture 1: Introduction of the Section

Lecture 2: Signal Denoising by Moving Average Filter

Lecture 3: Implementing Moving Average Filter in Python

Lecture 4: Gaussian Mean Filter

Lecture 5: Gaussian Mean Filter With Python

Lecture 6: Median Filter

Lecture 7: Median Filter in Python

Lecture 8: Removing Spiky Noise With Median Filter

Lecture 9: Removing Spiky Noise With Median Filter in Python Part01

Lecture 10: Removing Spiky Noise With Median Filter in Python Part02

Chapter 6: Complex Number Systems

Lecture 1: Introduction of Complex Numbers

Lecture 2: Complex Numbers in Python

Lecture 3: Mathematical Operations Part01

Lecture 4: Mathematical Operations Part02

Lecture 5: Mathematical Operations in Python

Lecture 6: Magnitude and Phase Calculations

Lecture 7: Magnitude and Phase Calculations in Python

Lecture 8: Complex Sine Wave

Lecture 9: Complex Sine Wave in Python

Chapter 7: Fourier Transform

Lecture 1: Introduction of the Section

Lecture 2: Combining Sine and Cosine Wave

Lecture 3: Generating Waves in Python

Lecture 4: Mechanism of Fourier Transform

Lecture 5: Step by Step Coding of Fourier Transform

Lecture 6: Fast Fourier Transform

Lecture 7: Fourier Transform of Signal With DC Component

Lecture 8: Amplitude and Power Spectrum

Lecture 9: Inverse Fourier Transform

Lecture 10: Application of Fourier Transform Part01

Lecture 11: Application of Fourier Transform Part02

Chapter 8: FIR Filter Design

Lecture 1: Introduction of the Section

Lecture 2: Introduction of Digital Filters

Lecture 3: Steps of Designing FIR Filters

Lecture 4: FIR Filter Design by Least Square Method

Lecture 5: FIR Filter Design by Window Method

Lecture 6: FIR Zero Shift Filter

Lecture 7: Low Pass FIR Filter

Lecture 8: Low Pass FIR Filter in Python

Lecture 9: High Pass FIR Filter

Lecture 10: High Pass FIR Filter in Python

Lecture 11: Band Pass FIR Filter

Lecture 12: Band Pass FIR Filter in Python

Lecture 13: Task for Students

Chapter 9: IIR Filter Design

Lecture 1: Introduction of the Section

Lecture 2: Introduction of IIR Filter

Lecture 3: IIR Butterworth Filter Design in Python

Lecture 4: Low Pass IIR Filter

Lecture 5: High Pass IIR Filter

Lecture 6: Band Pass IIR Filter

Lecture 7: Comparison Between FIR and IIR Filters

Lecture 8: Task for Students

Instructors

  • Zeeshan Ahmad
    Zeeshan Ahmad
    Machine Learning and Statistical Signal Processing

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 0 votes
  • 3 stars: 7 votes
  • 4 stars: 18 votes
  • 5 stars: 34 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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