Practical Python Wavelet Transforms (I) Fundamentals

Practical Python Wavelet Transforms (I) Fundamentals
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English

+ srt | Duration: 17 lectures (2h 5m) | Size: 1.25 GB[/center]

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

What you'll 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

Requirements
Basic Python programming experience needed
Basic knowledge on Jupyter notebook, Python data analysis and visualiztion are advantages, but are not required

Description
The Wavelet Transforms (WT) or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier transform, which transform 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

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 is a course series, in which one can learn Wavelet Transforms using word-real projects. The topics of this course series includes the following topics

Fundmentals of Wavelet Transforms (WT)

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 we will learn the main basic concepts concerning Wavelet transofrms, wavelets families and its members, Wavelet and scaling functions and their visualization, as well as setting up Python Wavelet Transform Environment. After this course, you will obtain the baisc knowledge and skills for further learning the advanced topics in the future courses of this series.

Who this course is for
Data Analysist, Engineers and Scientists
Signal Processing Engineers and Professionals
Machine Learning Engineers, Scientists and Professionals who are seeking advance algrothms
Acedemic faculties and students who study signal processing, data analysis and machine learning
Anyone who likes signal processing, data analysis,and advance algrothms for machine learning