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Practical Python Wavelet Transforms (I): Fundamentals

Real-World Projects with PyWavelets, Jupyter notebook, Numpy, Pandas, Matplotlib and Many More
3.8
3.8/5
(6 reviews)
2,222 students
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8.7

CourseMarks Score®

10.0

Freshness

6.6

Feedback

9.0

Content

Platform: Udemy
Video: 2h 9m
Language: English
Next start: On Demand

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CourseMarks Score®

8.7 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 4/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

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6.6 / 10
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Content Score

9.0 / 10
Video Score: 7.9 / 10
The course includes 2h 9m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

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This course contains:

0 article.
18 resources.
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0 test.

Table of contents

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 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, 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. 

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

Requirements

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

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

How much does the Practical Python Wavelet Transforms (I): Fundamentals course cost? Is it worth it?

The course costs $14.99. And currently there is a 25% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.

Does the Practical Python Wavelet Transforms (I): Fundamentals course have a money back guarantee or refund policy?

YES, Practical Python Wavelet Transforms (I): Fundamentals has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Practical Python Wavelet Transforms (I): Fundamentals course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Dr. Shouke Wei a SCAM or a TRUSTED instructor?

Dr. Shouke Wei has created 5 courses that got 30 reviews which are generally positive. Dr. Shouke Wei has taught 2,423 students and received a 4.2 average review out of 30 reviews. Depending on the information available, Dr. Shouke Wei is a TRUSTED instructor.
Professor
Nearly 20 years of research and teaching experience and 10 years of entrepreneur and management experience in computer modelling and simulation, big data analysis, machine learning algorithms; Ph.D. in Environment and Resource Management; Postdoctoral scientist, and Ph.D. supervisor in Environmental System Modelling; Research associate and Visiting scientist in Forest Hydrological Ecosystem Modelling; Industrial Professor, Adjunct Professor teaching AI and machine learning courses and Postgraduate supervisor in Deep reinforcement learning and Computer vision; Senior Research in R&D of real time monitoring and early warning system platform for water protection, human safety and health; Participated in or hodeling 12 international research projects; Participated as an invited key speaker in 12 scientific conferences and workshops; Having 27 software copyrights, 4 patents and over 40 pulications.

8.7

CourseMarks Score®

10.0

Freshness

6.6

Feedback

9.0

Content

Platform: Udemy
Video: 2h 9m
Language: English
Next start: On Demand

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