{"id":10313,"date":"2026-08-10T10:17:36","date_gmt":"2026-08-10T10:17:36","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-outlier-detection-in-python-video-edition-2024-12\/"},"modified":"2026-08-10T10:17:36","modified_gmt":"2026-08-10T10:17:36","slug":"oreilly-outlier-detection-in-python-video-edition-2024-12","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-outlier-detection-in-python-video-edition-2024-12\/","title":{"rendered":"Oreilly \u2013 Outlier Detection in Python, Video Edition 2024-12"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Outlier Detection in Python, Video Edition. This course teaches you how to identify unusual, interesting, or suspicious data in your data sets using Python. In addition to discovering patterns, data scientists must also recognize exceptions, as these anomalies often contain valuable insights, hidden problems, or new opportunities. In this course, you will learn practical methods for identifying points that deviate from the overall data pattern, even when these points are hidden among normal information. Topics include using Python\u2019s standard libraries, choosing appropriate methods, combining different techniques to improve accuracy, and effectively interpreting results. Working with numeric, batch, text, and time series data types is also covered. Anomaly detection is used in areas such as fraud detection, security analysis, and data quality control. The course is packed with real-world examples from a variety of industries, including finance, social media, and networking, and teaches essential tools like scikit-learn and PyOD. Prerequisites for this course include a basic understanding of statistics and the Python environment. By the end, you will be able to apply key algorithms and practical techniques to identify and analyze anomalies in a variety of data.<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"35:1-35:25\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"37:1-45:0\">\n<li data-sourcepos=\"37:1-37:54\"><span style=\"vertical-align: inherit\">Using Python libraries to identify outliers<\/span><\/li>\n<li data-sourcepos=\"38:1-38:42\"><span style=\"vertical-align: inherit\">Choosing the most appropriate outlier detection methods<\/span><\/li>\n<li data-sourcepos=\"39:1-39:49\"><span style=\"vertical-align: inherit\">Combining multiple outlier detection methods to improve results<\/span><\/li>\n<li data-sourcepos=\"40:1-40:33\"><span style=\"vertical-align: inherit\">Effective interpretation of outlier detection results<\/span><\/li>\n<li data-sourcepos=\"41:1-41:42\"><span style=\"vertical-align: inherit\">Working with numerical data to detect outliers<\/span><\/li>\n<li data-sourcepos=\"42:1-42:45\"><span style=\"vertical-align: inherit\">Working with batch data to detect outliers<\/span><\/li>\n<li data-sourcepos=\"43:1-43:47\"><span style=\"vertical-align: inherit\">Working with time series data to detect outliers<\/span><\/li>\n<li data-sourcepos=\"44:1-45:0\"><span style=\"vertical-align: inherit\">Working with text data to detect outliers<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"46:1-46:32\"><span style=\"vertical-align: inherit\">Who is this course suitable for?<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"48:1-53:0\">\n<li data-sourcepos=\"48:1-48:71\"><span style=\"vertical-align: inherit\">Python programmers who are familiar with tools like pandas and NumPy.<\/span><\/li>\n<li data-sourcepos=\"49:1-49:39\"><span style=\"vertical-align: inherit\">People who have basic knowledge in statistics.<\/span><\/li>\n<li data-sourcepos=\"50:1-50:71\"><span style=\"vertical-align: inherit\">Anyone interested in identifying anomalies and outliers in data.<\/span><\/li>\n<li data-sourcepos=\"51:1-51:68\"><span style=\"vertical-align: inherit\">Data scientists looking to learn outlier detection techniques.<\/span><\/li>\n<li data-sourcepos=\"52:1-53:0\"><span style=\"vertical-align: inherit\">Data analysts who want to improve their skills in identifying outliers.<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course details for Outlier Detection in Python, Video Edition<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/outlier-detection-in\/9781633436473VE\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/brett-kennedy\/\"><span style=\"vertical-align: inherit\">Brett Kennedy<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 19 hours and 35 minutes<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Introducing outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Outlier detection\u2019s place in machine learning <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Outlier detection in tabular data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Definitions of outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Trends in outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. How does this book teach outlier detection? <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Simple outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. One-dimensional categorical outliers: Rare values <\/span><br \/><span style=\"vertical-align: inherit\">\u200b\u200bChapter 2. Multidimensional outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Rare combinations of categorical values <\/span><br \/><span style=\"vertical-align: inherit\">\u200b\u200bChapter 2. Rare combinations of numeric values <\/span><br \/><span style=\"vertical-align: inherit\">\u200b\u200bChapter 2. Noise vs. inliers and outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Local and global outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Combining the scores of univariate tests <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Machine learning-based outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Types of algorithms <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Types of detectors <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. The outlier detection process <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Determining the types of outliers we are interested in <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Choosing the type of model to be used <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Collecting the data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Examining the data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Cleaning the data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Feature selection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Feature engineering <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Encoding categorical values \u200b\u200bChapter 4. <\/span><br \/><span style=\"vertical-align: inherit\">Scaling numeric values <\/span><br \/><span style=\"vertical-align: inherit\">\u200b\u200bChapter 4. Fitting a set of models and generating predictions <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Evaluating the models <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Setting up ongoing outlier detection systems <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Refitting the models as necessary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Outlier detection using scikit-learn <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Isolation Forest <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. LocalOutlierFactor (LOF) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. One-class SVM (OCSVM) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Elliptic Envelope <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Gaussian mixture models <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. BallTree and KDTree <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. The PyOD library <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Histogram-based Outlier Score (HBOS) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Empirical Cumulative Distribution Function (ECOD) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Copula-based outlier detection (COPOD) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Angle-based outlier detection (ABOD) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Clustering-based local outlier factor (CBLOF) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Local correlation integral (LOCI) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Connectivity-based outlier factor (COF) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Principal component analysis (PCA) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Subspace outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. FeatureBagging <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Cook\u2019s Distance <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Using SUOD for faster model training <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. The PYOD thresholds module <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Additional libraries and algorithms for outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. The alibi-detect library <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. The PyCaret library <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Local outlier probability (LoOP) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Local distance-based outlier factor (LDOF) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Extended Isolation Forest (EIF) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Outlier Detection Using In-degree Number (ODIN) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Clustering <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Entropy <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Association Rules <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Convex Hull <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Distance metric learning (DML) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. NearestSample <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Evaluating detectors and parameters <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Contour plots <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Visualizing subspaces in real-world data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Correlation between detectors with full real-world datasets <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Modifying real-world data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Testing with classification datasets <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Timing experiments <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Working with specific data types <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Special data types <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Text features <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Encoding categorical data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Scaling numeric values <\/span><br \/><span style=\"vertical-align: inherit\">\u200b\u200bChapter 9. Binning numeric data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Distance metrics <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Handling very large and very small datasets <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Data with many rows <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Working with very small datasets <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Synthetic data for outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Generating new synthetic data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Doping <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Simulations <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Training classifiers to distinguish real from fake data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Collective outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Preparing the data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Testing for duplicates <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Testing for gaps Chapter 12. <\/span><br \/><span style=\"vertical-align: inherit\">Testing for missing combinations <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Creating new tables to capture collective outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Identifying trends <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Unusual distributions <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Rolling windows features <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Tests for unusual numbers of point anomalies <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Explainable outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Post hoc explanations <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Interpretable outlier detectors <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Ensembles of outlier detectors <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Accuracy metrics with ensembles <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Methods to create ensembles <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Selecting detectors for an ensemble <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Scaling scores <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Combining scores <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Working with outlier detection predictions <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Examining the flagged outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Automating the process of sorting outlier detection results <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Semisupervised learning <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Regression testing <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 4. <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 16. Deep learning-based outlier detection <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 16. PyOD <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 16. Image data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 16. alibi-detect <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 16. Self-supervised learning for outlier detection with tabular data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 16. Summary <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 17. Time-series data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 17. Types of time-series outliers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 17. Tools for time-series data <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 17. Summary<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-983950 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/05\/Outlier-Detection-in-Python-Video-Edition.png\" alt=\"Outlier Detection in Python, Video Edition\" width=\"1242\" height=\"434\"><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Sample course video<\/span><\/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-164864-1\"><video class=\"wp-video-shortcode\" id=\"video-164864-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Outlier_Detection_in_Python_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; 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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<div dir=\"ltr\" style=\"text-align: left\">\n<h3><span style=\"vertical-align: inherit\">Installation Guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitles: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 720p<\/span><\/p>\n<\/div>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Outlier_Detection_in_Python_Video_Edition_2024-12.part1_Downloadly.ir.rar?nocache=1786137731\"><span style=\"vertical-align: inherit\">Download Part 1 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Outlier_Detection_in_Python_Video_Edition_2024-12.part2_Downloadly.ir.rar?nocache=1786137731\"><span style=\"vertical-align: inherit\">Download Part 2 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Outlier_Detection_in_Python_Video_Edition_2024-12.part3_Downloadly.ir.rar?nocache=1786137731\"><span style=\"vertical-align: inherit\">Download Part 3 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Outlier_Detection_in_Python_Video_Edition_2024-12.part4_Downloadly.ir.rar?nocache=1786137731\"><span style=\"vertical-align: inherit\">Download Part 4 \u2013 244 MB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">3.2 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Outlier Detection in Python, Video Edition. This course teaches you how to identify unusual, interesting, or suspicious data in your data sets using<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[87382,87383,87384,87385,87386,87387],"class_list":["post-10313","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-brett-kennedy","dgi_tag-course-outlier-detection-in-python-video-edition","dgi_tag-download-course-outlier-detection-in-python-video-edition","dgi_tag-download-outlier-detection-in-python-video-edition","dgi_tag-free-download-outlier-detection-in-python-video-edition","dgi_tag-free-outlier-detection-in-python-video-edition"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/10313","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\/10313\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=10313"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=10313"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=10313"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}