Practical Python Wavelet Transforms (I): Fundamentals

Real-World Projects with PyWavelets, Jupyter notebook, Numpy, Pandas, Matplotlib 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
Attention: Please read careful about the description, especially the last paragraph, before buying this course. 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:noise removal from the signalstrend analysis and forecastingdetection of abrupt discontinuities, change, or abnormal behavior, etc. andcompression of large amounts of datathe new image compression standard called JPEG2000 is fully based on waveletsdata encryption, i.e. secure the dataCombine it with machine learning to improve the modelling accuracyTherefore, it would be great for your future development if you could learn this great tool.  Practical 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): Fundamentals Discrete Wavelet Transform (DWT)Stationary 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 fundamental part of this course series, in which you will learn the basic concepts concerning Wavelet transforms, wavelets families and their members, wavelet 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. 

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