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Nikki Castle

Nikki Castle
Marketing operations specialist at DataScience.com.

Recent Posts

Nikki CastleMarch 22, 2018

Are Your Models Underperforming Post-Deployment?

Predictive models are at the center of data-informed decision-making strategies for successful enterprise companies today, but sometimes their performance doesn’t meet expectations. An effective...
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Nikki CastleMarch 8, 2018

Regression vs. Classification Algorithms

Machine learning algorithms can be split into different categories based on the format of their outputs. By far, the most commonly used are regression and classification algorithms. Find out how they...
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Nikki CastleFebruary 14, 2018

Crowd-Sourced Data and AI: A Perfect Pair

This Valentine's Day we're celebrating crowd-sourced data and artificial intelligence. Find out why they're such an ideal match.
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Nikki CastleFebruary 8, 2018

What is Semi-Supervised Learning?

Find out what semi-supervised machine learning algorithms are and how they compare to supervised and unsupervised machine learning methods.
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Nikki CastleJanuary 3, 2018

What is Artificial Intelligence?

Artificial intelligence gets a lot of hype, but what is it really? Learn more about the limitations and capabilities of AI in business applications.
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Nikki CastleDecember 1, 2017

See You at H2O World, 2017

Meet us at H2O World, 2017 in Mountain View from December 4-5.
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Nikki CastleNovember 22, 2017

Meet Us at Chief Data Scientist, USA

Meet us at Chief Data Scientist and Artificial Intelligence Officer, USA 2017 in San Francisco from November 29-30.
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Nikki CastleOctober 31, 2017

Supporting Your Digital Transformation

Find out how IT management best practices and data science work in tandem to support digital transformation.
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Nikki CastleJuly 20, 2017

R vs. Python: What Language is Best for Building Data Models?

Discover some of the most common reasons data scientists choose either R or Python for building data models.
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Nikki CastleJuly 13, 2017

Supervised vs. Unsupervised Machine Learning

Machine learning algorithms are split into two categories based on how they process data. Discover the difference between supervised and unsupervised learning.
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