- How to use Machine Learning for Anomaly Detection and Conditional Monitoring - Dec 16, 2020.
This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.
- Toward a More Effective Disease Outbreak Alert System: A Symptoms Approach to Biosurveillance [Nov 19 webinar] - Nov 12, 2020.
Learn how the use of more granular symptoms-level data combined with innovative statistical techniques has the potential to identify disease outbreaks faster while limiting false positives.
- How Data Scientists Can Train and Updates Models to Prepare for COVID-19 Recovery - Apr 28, 2020.
The COVID-19 pandemic has affected everything, and building predictions during this time is difficult. Data science teams need to update their models to prepare for the recovery, and know how to properly train 2020 data models to learn from the coronavirus anomaly.
- KDnuggets™ News 20:n14, Apr 8: Free Mathematics for Machine Learning eBook; Epidemiology Courses for Data Scientists - Apr 8, 2020.
Stop Hurting Your Pandas!; Python for data analysis... is it really that simple?!?; Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs; Build an app to generate photorealistic faces using TensorFlow and Streamlit; 5 Ways Data Scientists Can Help Respond to COVID-19 and 5 Actions to Avoid
- Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs - Apr 1, 2020.
From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.
- How To Painlessly Analyze Your Time Series - Mar 26, 2020.
The Matrix Profile is a powerful tool to help solve this dual problem of anomaly detection and motif discovery. Matrix Profile is robust, scalable, and largely parameter-free: we’ve seen it work for a wide range of metrics including website user data, order volume and other business-critical applications.
- Top 10 AI, Machine Learning Research Articles to know - Jan 30, 2020.
We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Here, we review a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.
- Anomaly Detection, A Key Task for AI and Machine Learning, Explained - Oct 21, 2019.
One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.
- What is Benford’s Law and why is it important for data science? - Aug 7, 2019.
Benford’s law is a little-known gem for data analytics. Learn about how this can be used for anomaly or fraud detection in scientific or technical publications.
- How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
- Build an Anomaly Detection Project [Free Guidebook] - Jun 14, 2018.
Learn how to find value and insight in outliers in the latest anomaly detection guidebook by Dataiku, which includes use cases, and step-by-step guidance (including code samples) to starting an anomaly detection project.
- Machine Learning Anomaly Detection: The Ultimate Design Guide - May 25, 2017.
Considering building a machine learning anomaly detection system for your high velocity business? Learn how with Anodot ultimate three-part guide.
- Finding “Gems” in Big Data - Apr 4, 2017.
Detecting anomalous cases in large datasets is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Learn how to do it online in "Anomaly Detection" course at Statistics.com.
- Introduction to Anomaly Detection - Apr 3, 2017.
This overview will cover several methods of detecting anomalies, as well as how to build a detector in Python using simple moving average (SMA) or low-pass filter.
- Analytics and Machine Learning training in Q2 - Mar 24, 2017.
Learn Anomaly Detection, Deep Learning, or Customer Analytics in R online at Statistics.com with top instructors who are leaders of the field. Use code 3CAP17 before March 30 to save $170.
- 17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
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- 4 ways to learn about Deep Learning, Anomaly Detection and more Data Science topics online at Statistics.com - Jan 11, 2017.
Online courses at Statistics.com are small, with rich and engaging content that includes readings, videos, quizzes, homework, projects, and practical work with software. Use promo code deepkdn17 to save.
- Statistics.com new courses: Anomaly Detection, Meta Analysis, IoT, Deep Learning, Spatial Analytics - Oct 5, 2016.
Five new courses from Statistics.com, fully online and asynchronous - interact with leading experts in private forums. Use promo code “kdn2016” for $50 off any course.
- Data Science vs Crime: Detecting Pickpocket Suspects from Transit Records - Sep 1, 2016.
A team of US and Chinese researchers has creatively used massive data collected by automated fare collectors for identifying thieves in the public transit systems. The system was tested in Beijing and was able to identify 93% of known pickpockets.
- A simple approach to anomaly detection in periodic big data streams - Aug 24, 2016.
We describe a simple and scaling algorithm that can detect rare and potentially irregular behavior in a time series with periodic patterns. It performs similarly to Twitter's more complex approach.
- Analytics, Security, Deep Learning, IoT, Data Science Online Courses - Aug 20, 2016.
Upcoming online courses include : Statistical and machine learning methods for detecting anomalies, identifying images, and processing data from sensors; Deep Learning; Internet of Things (IoT): Programming for Analytics; and Meta Analysis in R.
- 21 Must-Know Data Science Interview Questions and Answers, part 2 - Feb 20, 2016.
Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.
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- On Why Sequels Are Bad and Red Light Cameras Aren’t As Effective - Feb 3, 2016.
Regression to the mean is a statistical phenomenon whereby extreme observations will tend to decrease (regress) towards the mean on subsequent readings. Regression to the mean is essentially a result of selection bias, learn more about it.
- Understanding Rare Events and Anomalies: Why streaks patterns change - Jan 8, 2016.
We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!
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- Anomaly Detection in Predictive Maintenance with Time Series Analysis - Dec 9, 2015.
How can we predict something we have never seen, an event that is not in the historical data? This requires a shift in the analytics perspective! Understand how to standardization the time and perform time series analysis on sensory data.
- Strata + Hadoop World 2015 San Jose – Day 2 Highlights - Mar 10, 2015.
Strata + Hadoop World 2015 was a great conference, and here are key insights from some of the best sessions on day 2.
- Top KDnuggets tweets, Mar 2-8: 6 categories in the Hadoop Ecosystem; How PayPal uses Deep Learning to fight fraud - Mar 9, 2015.
How #PayPal uses #DeepLearning and detective work to fight #fraud; Beginning #deeplearning with 500 lines of Julia; Processing frameworks for Hadoop and 6 categories in the #Hadoop Ecosystem; KDnuggets Poll results: #Analytics, #DataMining, #DataScience salary income by region.
- Top KDnuggets tweets, Nov 10-11: R on its way to the top 10; Using #MachineLearning to Detect Abnormalities - Nov 12, 2014.
Statistical language R on its way to the top 10; California rules #DataScience, but there's a very long tail; How to Explain #BigData to Your Grandmother; Using #MachineLearning to Detect Abnormalities in Time Series Data.
- Top KDnuggets tweets, Oct 27-28: Twitter Breakout detection in the wild; Marc Andreessen on #BigData and finance - Oct 29, 2014.
Dilbert on inability of designers predict results of A/B tests; Marc Andreessen @pmarc, web pioneer, VC @a16z on #BigData, upending finance; Will Deep Learning take over Machine Learning, make other algorithms obsolete?;.@WillJHenry @data_nerd @KirkDBorne Data Scientists don't wear bowties!
- Top KDnuggets tweets, May 21-22: Outlier Detection for Temporal Data; Become a Big Data mgr with #ieMBD - May 23, 2014.
Outlier Detection for Temporal Data ; 1.5M #BigData managers will be needed - Become one with #ieMBD; Goldman Sachs Surveillance Analytics; InformationWeek 10 Big Data Pros To Follow On Twitter.
- Outlier Detection for Temporal Data - May 22, 2014.
Outlier Detection for Temporal Data covers topics in temporal outlier detection, which have applications in numerous fields. It starts with the basic topics then moves on to state of the art techniques in the field.
- What is numbersense – test yours - Mar 25, 2014.
Kaiser Fung, Marketing and Analytics expert, and author of "Numbersense" book, explains what is numbersense in the age of Big Data. Test yours.