- Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance - Dec 21, 2020.
A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
- 20 Core Data Science Concepts for Beginners - Dec 8, 2020.
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
- Data Cleaning: The secret ingredient to the success of any Data Science Project - Jul 1, 2020.
With an uncleaned dataset, no matter what type of algorithm you try, you will never get accurate results. That is why data scientists spend a considerable amount of time on data cleaning.
- How to Prepare Your Data - Jun 30, 2020.
This is an overview of structuring, cleaning, and enriching raw data.
- An Overview of Outlier Detection Methods from PyOD – Part 1 - Jun 27, 2019.
PyOD is an outlier detection package developed with a comprehensive API to support multiple techniques. This post will showcase Part 1 of an overview of techniques that can be used to analyze anomalies in data.
- Intuitive Visualization of Outlier Detection Methods - Feb 5, 2019.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own.
- Four Techniques for Outlier Detection - Dec 6, 2018.
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.
- How to Make Your Machine Learning Models Robust to Outliers - Aug 28, 2018.
In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
- 8 Common Pitfalls That Can Ruin Your Prediction - Mar 21, 2018.
A good prediction can help your work and make it easier. But how can you be sure that your prediction is good? Here are some common pitfalls that you should avoid.
- How To Debug Your Approach To Data Analysis - Dec 29, 2017.
Seven common biases that influence how we understand, use, and interpret the world around us.
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- Top 6 errors novice machine learning engineers make - Oct 30, 2017.
What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
- KDnuggets™ News 17:n07, Feb 22: 17 Must-Know Data Science Interview Q&A; Removing Outliers in Python - Feb 22, 2017.
Also Removing Outliers Using Standard Deviation in Python; Natural Language Processing Key Terms, Explained; Data Scientists Strongly Oppose Trump Immigration Ban.
- Removing Outliers Using Standard Deviation in Python - Feb 16, 2017.
Standard Deviation is one of the most underrated statistical tools out there. It’s an extremely useful metric that most people know how to calculate but very few know how to use effectively.
- 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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- 3 methods to deal with outliers - Jan 3, 2017.
In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.
- Data Science Basics: What Types of Patterns Can Be Mined From Data? - Dec 14, 2016.
Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.
- A Neat Trick to Increase Robustness of Regression Models - Aug 22, 2016.
Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?
- 20 Questions to Detect Fake Data Scientists - Jan 1, 2016.
Hiring Data Scientists is no easy job, particularly when there are plenty of fake posers. Here is a handy list of questions to help separate the wheat from the chaff.
- 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.