Practical Python Wavelet Transforms (I): Fundamentals

小波变换 (WT) 或小波分析可能是克服傅里叶变换缺点的最新解决方案，它在周期（或频率）中变换信号而不损失时间分辨率。在信号处理方面，WT 提供了一种将感兴趣的输入信号分解为一组基本波形（即“小波”）的方法，然后通过检查这些小波的系数（或权重）来分析信号。

小波变换可用于平稳和非平稳信号，包括但不限于以下

从信号中去除噪声

趋势分析和预测g

检测突然的不连续、变化或异常行为等，以及

压缩大量数据

名为JPEG2000的新图像压缩标准完全基于小波

数据加密，即保护数据

与机器学习相结合，提高建模精度

因此，如果您能学习这个伟大的工具，对您未来的发展将是非常好的。Praclal Python Wavelet Transforms 是一个课程系列，您可以在其中使用真实的项目来学习小波变换。本课程系列的主题包括以下主题

小波变换基础 (WT)

离散小波变换 (DWT)

平稳小波变换 (SWT)

多分辨率分析 (MRA)

小波包变换 (WPT)

最大重叠离散小波变换 (MODWT)

基于MODWT（MODWTMRA）的多分辨率分析

本课程是本课程系列的基础部分，我们将学习有关小波变换、小波族及其成员、小波和缩放函数及其可视化的主要基本概念，以及设置 Python 小波变换环境。完成本课程后，您将获得基础知识和技能，以进一步学习本系列未来课程的高级主题。

本课程适用于
数据分析、工程师和科学家
信号处理工程师和专业人员
机器学习工程师、科学家和专业人员谁正在寻求高级算法
研究信号处理、数据分析和机器学习的学院和学生
任何喜欢信号处理、数据分析的人，以及用于机器学习的高级算法

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

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 17 lectures (2h 5m) | Size: 1.25 GB

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