{"id":15825,"date":"2026-08-10T11:59:02","date_gmt":"2026-08-10T11:59:02","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/elsevier-dimensionality-reduction-in-machine-learning-2025\/"},"modified":"2026-08-10T11:59:02","modified_gmt":"2026-08-10T11:59:02","slug":"elsevier-dimensionality-reduction-in-machine-learning-2025","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/elsevier-dimensionality-reduction-in-machine-learning-2025\/","title":{"rendered":"Elsevier \u2013 Dimensionality Reduction in Machine Learning 2025"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2>Descriptions<\/h2>\n<p>Dimensionality Reduction in Machine Learning, Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various real-world contexts. The book provides a comprehensive overview of key techniques such as Principal Component Analysis (PCA), t-SNE, autoencoders, and manifold learning, explaining their theoretical foundations and practical implementation. Readers will learn how to select and apply the right dimensionality reduction method for different types of data, improve model performance, and visualize high-dimensional datasets. This resource is ideal for students, researchers, and practitioners seeking to master dimensionality reduction for machine learning and data science applications.<\/p>\n<h3>Book features<\/h3>\n<ul>\n<li>Understand the mathematical foundations and programming aspects of dimensionality reduction algorithms.<\/li>\n<li>Compare and implement key techniques such as PCA, t-SNE, autoencoders, and manifold learning.<\/li>\n<li>Select appropriate dimensionality reduction methods for different data types and tasks.<\/li>\n<li>Improve machine learning model performance by reducing data dimensionality.<\/li>\n<li>Visualize and interpret high-dimensional datasets effectively.<\/li>\n<\/ul>\n<h3>Who this Book is for<\/h3>\n<ul>\n<li>Students studying machine learning, data science, or artificial intelligence.<\/li>\n<li>Researchers seeking a comprehensive reference on dimensionality reduction.<\/li>\n<li>Data scientists and practitioners applying dimensionality reduction in real-world projects.<\/li>\n<li>Anyone interested in understanding and visualizing high-dimensional data.<\/li>\n<\/ul>\n<h3>Specificatoin of Dimensionality Reduction in Machine Learning<\/h3>\n<ul>\n<li>Publisher : <a href=\"https:\/\/href.li\/?https:\/\/shop.elsevier.com\/books\/dimensionality-reduction-in-machine-learning\/rad\/978-0-443-32818-3\" target=\"_blank\" rel=\"noopener\">Elsevier<\/a><\/li>\n<li>Teacher : <a href=\"https:\/\/downloadlynet.ir\/tag\/jamal-amani-rad\">Jamal Amani Rad<\/a><\/li>\n<li>Language : English<\/li>\n<li>Level : All Levels<\/li>\n<li>Pages: 314<\/li>\n<li>Chapters: 11<\/li>\n<li>Format: PDF<\/li>\n<\/ul>\n<h3>Content of Dimensionality Reduction in Machine Learning<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1026107\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/12\/Dimensionality-Reduction-in-Machine-Learning.c1-scaled.jpeg\" alt=\"Dimensionality Reduction in Machine Learning\" width=\"845\" height=\"2560\"> <img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1026211\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/12\/DimensionalityReductioninMachineLearning.c2-scaled.webp\" alt=\"\" width=\"845\" height=\"2560\"><\/p>\n<h3>Pictures<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1026108\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/12\/Dimensionality-Reduction-in-Machine-Learning.d.jpeg\" alt=\"Dimensionality Reduction in Machine Learning\" width=\"866\" height=\"196\"><\/p>\n<h3>Installation Guide<\/h3>\n<p>Extract the files and watch with your favorite Software<\/p>\n<h3>Download Links<\/h3>\n<p><strong>Downloadly<\/strong><\/p>\n<p><a href=\"https:\/\/dl4.downloadly.ir\/Files\/Elearning\/Elsevier_Dimensionality_Reduction_in_Machine_Learning_2025_Downloadly.ir.rar?nocache=1785940620\" target=\"_blank\" rel=\"noopener\">Download- 13 MB<\/a><\/p>\n<p><strong>Rapidgator<\/strong><\/p>\n<p><a href=\"https:\/\/rapidgator.net\/file\/ffe10dcb1d974b397c2f6e5fe30cb695\/Elsevier_Dimensionality_Reduction_in_Machine_Learning_2025_Downloadly.ir.rar.html\" target=\"_blank\" rel=\"noopener\">Download- 13 MB<\/a><\/p>\n<h5>Password file(s): <a>www.downloadly.ir<\/a><\/h5>\n<h3>File size<\/h3>\n<p>13 MB<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Descriptions Dimensionality Reduction in Machine Learning, Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of di<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[127485],"dgi_tag":[127633,127634,127635,127636,127637,127638,127639,127640,127641],"class_list":["post-15825","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-ebook","dgi_tag-dimensionality-reduction-in-machine-learning","dgi_tag-dimensionality-reduction-in-machine-learning-download","dgi_tag-dimensionality-reduction-in-machine-learning-free","dgi_tag-dimensionality-reduction-in-machine-learning-free-download","dgi_tag-download-dimensionality-reduction-in-machine-learning","dgi_tag-elsevier-dimensionality-reduction-in-machine-learning","dgi_tag-free-dimensionality-reduction-in-machine-learning","dgi_tag-free-download-dimensionality-reduction-in-machine-learning","dgi_tag-jamal-amani-rad"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/15825","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\/15825\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=15825"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=15825"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=15825"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}