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World-real Projects with PyWavelets, Jupyter notebook, Pandas and Many More

What you will learn

Difference between time series and Signals

Basic concepts on waves

Basic concepts of Fourier Transforms

Basic concepts of Wavelet Transforms

Classification and applications of Wavelet Transforms

Setting up Python wavelet transform environment

Built-in Wavelet Families and Wavelets in PyWavelets

Approximation discrete wavelet and scaling functions and their visuliztion

Description

The Wavelet Transforms (WT)  or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier Transform (FT). WT transforms a signal in period (or frequency) without losing time resolution.  In the signal processing context, WT provides a method to decompose an input signal of interest into a set of elementary waveforms, i.e. “wavelets”., and then  analyze the signal by examining the coefficients (or weights) of these wavelets.

Wavelets transform can be used for stationary and nonstationary signals, including but not limited to the following:


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📌 We are thrilled to unveil this latest course Practical Python Wavelet Transforms (I): Fundamentals which is designed to unlock your full potential and propel you towards success. 🚀

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  • noise removal from the signals
  • trend analysis and forecationg
  • detection of abrupt discontinuities, change, or abnormal behavior, etc. and
  • compression of large amounts of data
    • the new image compression standard called JPEG2000 is fully based on wavelets
  • data encryption,i.e. secure the data
  • Combine it with machine learning to improve the modelling accuracy

Therefore, it would be great for your future development if you could learn this great tool.  Practiclal Python Wavelet Transforms includes a series of courses, in which one can learn Wavelet Transforms using word-real cases. The topics of  this course series includes the following topics:

  • Part (I): Fundmentals
  • Discrete Wavelet Transform (DWT)
  • Sationary Wavelet Transform (SWT)
  • Multiresolutiom Analysis (MRA)
  • Wavelet Packet Transform (WPT)
  • Maximum Overlap Discrete Wavelet Transform (MODWT)
  • Multiresolutiom Analysis based on MODWT (MODWTMRA)

This course is the fundmental part of this course series, in which you will learn the basic concepts concerning Wavelet transofrms, wavelets families and their members, savelet and scaling functions and their visualization, as well as setting up Python Wavelet Transform Environment. After this course, you will obtain the basic knowledge and skills for the advanced topics in the future courses of this series. However, only the free preview parts  in this course are prerequisites for the advanced topics of this series.

English
language

Content

Introduction

Introduction

Basic Concepts of Wavelet Transforms

Time Seires and Signals
Basic Concepts of Waves
Concepts of Fourier Transforms
Concepts of Wavelet Transforms
Wavelet Transform Classification
Applications of Wavelet Transforms

Setting up PyWavelets Environment

Installing Anaconda Python
Adding Anaconda Powershell on Right-click Menu of Windows (Optional)
Required Packages
Basic Operations of Working Directory
Basic Operations of Jupyter Notebook

PyWavelets and its Built-in Wavelets

Introduction to PyWavelets
PyWavelets Built-in Wavelets Families
Discrete Wavelets Properties
Continuous Wavelet Properties
Approximating Wavelet and Scaling Functions
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