- 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.
- Predicting the President: Two Ways Election Forecasts Are Misunderstood - Mar 27, 2020.
With election cycles always seeming to be in season, predictions on outcomes remain intriguing content for the voting citizens. Misinterpretation of election forecasts also runs rampant, and can impact perceptions of candidates and those who post these predictions. A better fundamental understanding of probability can help improve our collective notion of futurism, and how we monitor elections.
- Graph Neural Network model calibration for trusted predictions - Mar 24, 2020.
In this article, we’ll talk about calibration in graph machine learning, and how it can help to build trust in these powerful new models.
- Do You Trust and Understand Your Predictive Models? - Feb 4, 2020.
To help practitioners make the most of recent and disruptive breakthroughs in debugging, explainability, fairness, and interpretability techniques for machine learning read “An Introduction to Machine Learning Intrepretability Second Edition”. Download this report now.
- How Concerned Should You be About Predictor Collinearity? It Depends… - Aug 15, 2019.
Predictor collinearity (also known as multicollinearity) can be problematic for your regression models. Check out these rules of thumb about when, and when not, to be concerned.
- The Persuasion Paradox – How Computers Optimize their Influence on You - Feb 16, 2019.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade.
- Solve epileptic seizure prediction! Participate at epilepsyecosystem.org - Aug 14, 2018.
Around twenty million people worldwide suffer from drug-resistant epilepsy and the unpredictability of seizures is one of the major factors affecting the quality of life of people with epilepsy.
- Identifying Variables That Might Be Better Predictors - Feb 2, 2017.
This blog serves to expand on the approach that the data science team uses to identify (and quantify) which variables and metrics are better predictors of performance.
- arXiv Paper Spotlight: Stealing Machine Learning Models via Prediction APIs - Nov 28, 2016.
Despite their confidentiality, machine learning models which have public-facing APIs are vulnerable to model extraction attacks, which attempt to "steal the ingredients" and duplicate functionality. The paper at hand investigates.
- Predictive Science vs Data Science - Nov 22, 2016.
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
- Combining Different Methods to Create Advanced Time Series Prediction - Nov 16, 2016.
The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!
- 13 Forecasts on Artificial Intelligence - Nov 15, 2016.
Once upon a time, Artificial Intelligence (AI) was the future. But today, human wants to see even beyond this future. This article try to explain how everyone is thinking about the future of AI in next five years, based on today’s emerging trends and developments in IoT, robotics, nanotech and machine learning.
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- Predicting Future Human Behavior with Deep Learning - Sep 30, 2016.
Carl Vondrick, MIT researcher, who studies computer vision and machine learning, discusses how to use Big Data with minimal annotations and applications to predictive vision and scene understanding.
- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
- Barley, Hops, and Bayes: Predicting The World Beer Cup - Jul 26, 2016.
This post covers predicting award counts by the United States in an international beer competition. Exploratory data analysis and Bayes methods are also supported.
- A Brief Primer on Linear Regression – Part III - Jul 5, 2016.
This third part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
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- A Brief Primer on Linear Regression – Part 2 - Jun 13, 2016.
This second part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
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- A Brief Primer on Linear Regression – Part 1 - Jun 6, 2016.
This introduction to linear regression discusses a simple linear regression model with one predictor variable, and then extends it to the multiple linear regression model with at least two predictors.
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- Predicting Popularity of Online Content - May 30, 2016.
A look at predicting what makes online content popular, with a particular focus on images, especially selfies.
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- HPE Haven OnDemand and Microsoft Azure Machine Learning: Power Tools for Developers and Data Scientists - Mar 29, 2016.
While both HPE and Microsoft machine learning platforms offer numerous possibilities for developers and data scientists, HPE Haven OnDemand is a diverse collection of APIs for interacting with data designed with flexibility in mind, allowing developers to quickly perform data tasks in the cloud.
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- The Machine Learning Problem of The Next Decade - Feb 26, 2016.
How can businesses integrate imperfect machine-learning algorithms into their workflow?
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- A Neural Network in 11 lines of Python - Oct 30, 2015.
A bare bones neural network implementation to describe the inner workings of back-propagation.
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- Gaming Analytics Summit 2015, San Francisco – Day 1 Highlights - May 8, 2015.
Highlights from the presentations by Gaming Analytics leaders from Facebook, Turbine/Warner Bros Games, and Sega on day 1 of Gaming Analytics Innovation Summit 2015 in San Francisco.
- Interview: Haile Owusu, Mashable on Riding the Wave of Viral Content - Apr 29, 2015.
We discuss Mashable’s milestones, data-driven digital publishing, digital media tracking, viral prediction, and Mashable Velocity.
- Cloud Machine Learning Wars: Amazon vs IBM Watson vs Microsoft Azure - Apr 16, 2015.
Amazon recently announced Amazon Machine Learning, a cloud machine learning solution for Amazon Web Services. Able to pull data effortlessly from RDS, S3 and Redshift, the product could pose a significant threat to Microsoft Azure ML and IBM Watson Analytics.
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- Interview: Alessandro Gagliardi, Glassdoor on the Indispensable Skills for Data Scientists - Apr 1, 2015.
We discuss Analytics at Glassdoor, important lessons, major factors affecting job satisfaction, challenges of working on Twitter Data, indispensable components of Data Science education.
- Dataiku Data Science Studio - Aug 26, 2014.
Data Science Studio (DSS) from Dataiku is a complete Data Science software tool for developers and analysts,
which significantly shortens the time-consuming load-clean-train-test-deploy cycles of building predictive applications.
A community edition and a free trial available.
- Interview: Saikat Mukherjee, ShareThis on Why Marketers can no longer Ignore Social TV? - Aug 20, 2014.
We discuss the role of Analytics at ShareThis, the emergence of Social TV, better user behavior insights through Social TV, major challenges with Social TV analytics, interesting insights, future trends, recommendation and more.
- Interview: Kavita Ganesan, FindiLike on Building Decision Support Systems based on User Opinions - Jul 27, 2014.
We discuss the founding story of FindiLike, Opinion-driven Decision Support Systems (ODSS), challenges in analyzing user opinions, future of Sentiment Analysis, favorite books and more.
- Innocentive Challenge: Novel Approaches for Predicting Life Expectancy - Jul 21, 2014.
Help develop novel methods for life expectancy prediction without using traditional medical records, invasive tests, or examinations in this Innocentive Challenge. Submissions due by August 4th.
- Interview: Dave Marvit, Innovation Strategy Consultant, Fujitsu on Privacy and Sentiment Analysis challenges - Jul 9, 2014.
We discuss the modern sentiment analysis challenges, how to address privacy concerns, Big Data predictions and more.
- Deep Learning with H2O, May 21 Webcast - May 1, 2014.
H2O is Google-scale open source machine learning engine for R and Big Data. Learn how Deep Learning in H2O is unlocking never before seen performance for prediction - May 21.