{"id":2606,"date":"2026-08-10T08:57:38","date_gmt":"2026-08-10T08:57:38","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-practical-python-wavelet-transforms-i-fundamentals-2022-3\/"},"modified":"2026-08-10T08:57:38","modified_gmt":"2026-08-10T08:57:38","slug":"udemy-practical-python-wavelet-transforms-i-fundamentals-2022-3","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-practical-python-wavelet-transforms-i-fundamentals-2022-3\/","title":{"rendered":"Udemy \u2013 Practical Python Wavelet Transforms (I): Fundamentals 2022-3"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 dir=\"ltr\">Description<\/h2>\n<p dir=\"ltr\">Practical Python Wavelet Transforms (I): Fundamentals is a training course on Wavelet Transforms concepts published by Udemy Online Academy. Wavelet transform (WT) or wavelet analysis is probably the newest solution to overcome the shortcomings of Fourier transform (FT). Wavelet transform (WT) transforms a signal in period (or frequency) without losing time resolution. In the field of signal processing, wavelet transform (WT) provides a method for decomposing an input signal of interest into a set of elementary waveforms, i.e. wavelets, and then analyzing the signal by examining the coefficients (or weights) of these wavelets. So, if you can learn this great tool, it will be great for your future development.<\/p>\n<p dir=\"ltr\">Wavelet transform can be used for stationary and non-stationary signals such as removing noise from signals, trend analysis and forecasting, detecting sudden discontinuities, abnormal change or behavior, etc., compressing large amounts of data, encoding data, i.e. Use data security and combine it with machine learning to improve modeling accuracy. In this tutorial you will learn wavelet transform (WT) using real word modes. In this valuable course, you will get to know the basic concepts about wavelet transform, wavelet family and their members, wavelet functions and their scaling and visualization, as well as setting up the Python wavelet transform environment. After completing this course, you will be able to learn more advanced topics of wavelet transforms.<\/p>\n<h3 dir=\"ltr\">What you will learn in Practical Python Wavelet Transforms (I): Fundamentals:<\/h3>\n<ul dir=\"ltr\">\n<li>Difference between time series and signals<\/li>\n<li>Basic concepts about waves<\/li>\n<li>Basic Concepts of Wavelet Transform (WT)<\/li>\n<li>Classification and Applications of Wavelet Transform (WT)<\/li>\n<li>Approximation of discrete wavelet functions and their scaling and visualization<\/li>\n<li>And \u2026<\/li>\n<\/ul>\n<h3 dir=\"ltr\">Course specifications<\/h3>\n<p dir=\"ltr\">Publisher: <a href=\"https:\/\/href.li\/?https:\/\/www.udemy.com\/course\/practical-python-wavelet-transform-i-fundamentals\/?couponCode=LETSLEARNNOW\" target=\"_blank\" rel=\"noopener\">Udemy<\/a><br \/>Instructors: <a href=\"https:\/\/downloadly.ir\/tag\/dr-shouke-wei\/\" target=\"_blank\" rel=\"noopener\">Dr. Shouke Wei<\/a><br \/>Language: English<br \/>Level: Introductory<br \/>Number of Lessons: 17<br \/>Duration: 2 hours and 5 minutes<\/p>\n<h3 dir=\"ltr\">Course topics on 2022\/4<\/h3>\n<p dir=\"ltr\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-892924\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2024\/03\/Practical-Python-Wavelet-Transforms-I-Fundamentals-Content.jpg\" alt=\"Practical Python Wavelet Transforms (I): Fundamentals Content\" width=\"780\" height=\"332\"><\/p>\n<h3 dir=\"ltr\">Practical Python Wavelet Transforms (I): Fundamentals Prerequisites<\/h3>\n<p dir=\"ltr\">Basic Python programming experience needed<br \/>Basic knowledge on Jupyter notebook, Python data analysis and visualiztion are advantages, but are not required<\/p>\n<h3 dir=\"ltr\">Pictures<\/h3>\n<p dir=\"ltr\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-892923\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2024\/03\/Practical-Python-Wavelet-Transforms-I-Fundamentals.jpg\" alt=\"Practical Python Wavelet Transforms (I): Fundamentals\" width=\"780\" height=\"392\"><\/p>\n<h3 dir=\"ltr\">Practical Python Wavelet Transforms (I): Fundamentals introduction video<\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-121864-1\"><video class=\"wp-video-shortcode\" id=\"video-121864-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Practical_Python_Wavelet_Transforms_I_Fundamentals_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Practical_Python_Wavelet_Transforms_I_Fundamentals_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Practical_Python_Wavelet_Transforms_I_Fundamentals_Downloadly.ir.mp4?nocache=1786095817349\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Practical_Python_Wavelet_Transforms_I_Fundamentals_Downloadly.ir.mp4<\/a><\/video><\/mediaelementwrapper><\/div>\n<div class=\"mejs-layers\">\n<div class=\"mejs-poster mejs-layer\" style=\"display: none; width: 100%; height: 100%;\"><\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-loading\"><span class=\"mejs-overlay-loading-bg-img\"><\/span><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-error\"><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer mejs-overlay-play\" style=\"width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-button\" role=\"button\" tabindex=\"0\" aria-label=\"Play\" aria-pressed=\"false\"><\/div>\n<\/div>\n<\/div>\n<div class=\"mejs-controls\">\n<div class=\"mejs-button mejs-playpause-button mejs-play\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Play\" aria-label=\"Play\" tabindex=\"0\"><\/button><\/div>\n<div class=\"mejs-time mejs-currenttime-container\" role=\"timer\" aria-live=\"off\"><span class=\"mejs-currenttime\">00:00<\/span><\/div>\n<div class=\"mejs-time-rail\"><span class=\"mejs-time-total mejs-time-slider\" role=\"slider\" tabindex=\"0\" aria-label=\"Time Slider\" aria-valuemin=\"0\" aria-valuemax=\"0\" aria-valuenow=\"0\" aria-valuetext=\"00:00\"><span class=\"mejs-time-buffering\" style=\"display: none;\"><\/span><span class=\"mejs-time-loaded\"><\/span><span class=\"mejs-time-current\"><\/span><span class=\"mejs-time-hovered no-hover\"><\/span><span class=\"mejs-time-handle\"><span class=\"mejs-time-handle-content\"><\/span><\/span><span class=\"mejs-time-float\"><span class=\"mejs-time-float-current\">00:00<\/span><span class=\"mejs-time-float-corner\"><\/span><\/span><\/span><\/div>\n<div class=\"mejs-time mejs-duration-container\"><span class=\"mejs-duration\">00:00<\/span><\/div>\n<div class=\"mejs-button mejs-volume-button mejs-mute\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Mute\" aria-label=\"Mute\" tabindex=\"0\"><\/button><a href=\"javascript:void(0);\" class=\"mejs-volume-slider\" aria-label=\"Volume Slider\" aria-valuemin=\"0\" aria-valuemax=\"100\" role=\"slider\" aria-orientation=\"vertical\"><span class=\"mejs-offscreen\">Use Up\/Down Arrow keys to increase or decrease volume.<\/span><\/p>\n<div class=\"mejs-volume-total\">\n<div class=\"mejs-volume-current\" style=\"bottom: 0px; height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h3 dir=\"ltr\"><span class=\"notranslate\">Installation guide<\/span><\/h3>\n<p dir=\"ltr\">After Extract, watch with your favorite Player.<\/p>\n<p dir=\"ltr\">English subtitle<\/p>\n<p dir=\"ltr\">Quality: 720p<\/p>\n<h3 dir=\"ltr\">Download link<\/h3>\n<p dir=\"ltr\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Udemy_Practical_Python_Wavelet_Transforms_I_Fundamentals_2022-3.part1_Downloadly.ir.rar?nocache=1786095816\">Download Part 1 \u2013 1 GB<\/a><\/p>\n<p dir=\"ltr\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Udemy_Practical_Python_Wavelet_Transforms_I_Fundamentals_2022-3.part2_Downloadly.ir.rar?nocache=1786095816\">Download Part 2 \u2013 214 MB<\/a><\/p>\n<h5 dir=\"ltr\">File password (s):&nbsp;<a>www.downloadly.ir<\/a><\/h5>\n<h3 dir=\"ltr\">Size<\/h3>\n<p dir=\"ltr\">1.2 GB<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Practical Python Wavelet Transforms (I): Fundamentals is a training course on Wavelet Transforms concepts published by Udemy Online Academy. Wavelet<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[26109,26110,26111,26112,26113,26114,26115,26116,26117,26118],"class_list":["post-2606","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-dr-shouke-wei","dgi_tag-practical-python-wavelet-transforms-i-fundamentals","dgi_tag-practical-python-wavelet-transforms-i-fundamentals-download","dgi_tag-practical-python-wavelet-transforms-i-fundamentals-free","dgi_tag-practical-python-wavelet-transforms-i-fundamentals-free-download","dgi_tag-udemy-practical-python-wavelet-transforms-i-fundamentals","dgi_tag-udemy-practical-python-wavelet-transforms-i-fundamentals-download","dgi_tag-udemy-practical-python-wavelet-transforms-i-fundamentals-english","dgi_tag-udemy-practical-python-wavelet-transforms-i-fundamentals-free","dgi_tag-udemy-practical-python-wavelet-transforms-i-fundamentals-free-download"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/2606","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/2606\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=2606"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=2606"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=2606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}