- Don’t Touch a Dataset Without Asking These 10 Questions - Sep 20, 2021.
Selecting the right dataset is critical for the success of your AI project.
Datasets, Distribution, Outliers, Privacy, Standardization
- 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.
AI, Deployment, Explainable AI, Machine Learning, Modeling, Outliers, Production, Python
- 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.
Beginners, Bias, Cross-validation, Data Science, Data Visualization, Data Wrangling, Outliers, PCA, Variance
- 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.
Data Cleaning, Data Preparation, Data Science, Outliers, Python
- How to Prepare Your Data - Jun 30, 2020.
This is an overview of structuring, cleaning, and enriching raw data.
Data Preparation, Data Preprocessing, Dimensionality Reduction, Missing Values, Outliers
- 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.
Algorithms, Big Data, Outliers, Python
- 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.
Cheat Sheet, Outliers, Python
- 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.
DBSCAN, Knime, Outliers, Python
- 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.
Machine Learning, Modeling, Outliers
- 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.
Advice, Data Science, Outliers, Overfitting, Predictive Analytics
- 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.
Beginners, Machine Learning, Mistakes, Outliers, Regression, Regularization, Time Series
- 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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Anomaly Detection, Bias, Classification, Data Science, Donald Trump, Interview Questions, Outliers, Overfitting, Variance
- 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.
Machine Learning, Outliers, Statistics
- 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.
Beginners, Classification, Data Science, Frequent Pattern Mining, Outliers, Regression
- 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?
CleverTap, Linear Regression, Outliers, Overfitting, Regression
- 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.
Data Scientist, Data Visualization, import.io, Kirk D. Borne, Machine Learning, Outliers