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  ML model optimization with ensemble learning, retraining

  Making ML models better post-deployment can be accomplished. Learn the ins and outs of two key techniques: ensemble learning and frequent model retraining.

  Arcitura Education • Aug. 20, 2021

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  3 ways to evaluate and improve machine learning models

  Training performance evaluation, prediction performance evaluation and baseline modeling can refine machine learning models. Learn how they work together to improve predictions.

  Arcitura Education • July 28, 2021

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  Discover 2 unsupervised techniques that help categorize data

  Two unsupervised techniques -- category discovery and pattern discovery -- solve ML problems by seeking similarities in data groups, rather than a specific value.

  Arcitura Education • June 28, 2021

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  2 supervised learning techniques that aid value predictions

  Learn how two supervised machine learning techniques -- numerical prediction and category prediction -- work to predict values and, thus, can aid model training.

  Arcitura Education • June 16, 2021

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  Wrangling data with feature discretization, standardization

  A variety of techniques help make data useful in machine learning algorithms. This article looks into two such data-wrangling techniques: discretization and standardization.

  Arcitura Education • June 8, 2021

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  2 data-wrangling techniques for better machine learning

  Before data can be usefully inputted into algorithms, it must first be prepared. Learn two of the techniques that do the job and make machine learning work.

  Arcitura Education • May 11, 2021

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  Moving beyond NLP to make chatbots smarter

  Machine reasoning could help chatbots better understand context, which is crucial to understanding human emotions and formulating emotionally relevant responses.

  Kathleen Walch, Cognilytica • April 29, 2021

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  How feature selection, extraction improve ML predictions

  In this discussion of machine learning patterns, learn how feature selection and feature extraction help make data more useful and, thus, improve predictions.

  Arcitura Education • April 22, 2021

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  Associativity, graphical summary computations aid ML insights

  Associativity computation and graphical summary computation allow for more complex insights, and in turn improve predictions. Explore how these ML techniques work in practice.

  Arcitura Education • April 12, 2021

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  Common ML patterns: Central tendency and variability

  Four common patterns provide approaches to solving machine-learning problems. Learn how two -- central tendency computation and variability computation -- work.

  Arcitura Education • March 23, 2021

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  Unsupervised machine learning: Dealing with unknown data

  Learn how machine learning works when dealing with unclassified, unlabeled data sets and how, using certain algorithms and other practices, the system can learn on its own.

  Arcitura Education • March 6, 2021

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  The supervised approach to machine learning

  In part 2 of our machine learning tutorial, learn how to use the supervised learning approach to machine learning to produce the best predictions.

  Arcitura Education • March 6, 2021

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  Free machine learning course: Using ML algorithms, practices and patterns

  This 13-lesson series offers an overview of machine learning and its applications for those hoping to break into the machine learning job market or learn more about this technology.

  Arcitura Education • Feb. 19, 2021

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  Introduction to using machine learning

  The first part of our machine learning series, excerpted from training materials for Arcitura's Machine Learning Specialist certification, introduces algorithms, models and model training.

  Arcitura Education • Feb. 18, 2021

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  A basic design pattern for image recognition

  Learn how a design pattern based on convolutional neural networks can be adapted to create a visual graphics generator model for image recognition.

  Andrew Ferlitsch, Manning Publications • Nov. 13, 2020

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  Does technology increase the problem of racism and discrimination?

  According to a publication from the MIT Technology Review, technology promotes racism. Most facial recognition algorithms discriminate against the Black population. And even certain concepts or technological terminology tend to be offensive, highlighting "white supremacy."

  Lizzette Pérez Arbesú • Aug. 17, 2020

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  Healthcare and AI are a good pair -- until vendors overpromise

  Excessive promotion and grandiose promises from vendors like IBM are making it hard to accurately judge what the proper role of AI in healthcare should be.

  Ed Burns • Oct. 26, 2018

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  Current state of AI is poorly understood by the public

  Current applications of AI revolve entirely around machine learning. But researcher Zachary Lipton says that's not the message you get from most media coverage of AI.

  Ed Burns • Oct. 1, 2018

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  Reskilling the analytics team: Math, science and creativity

  Technical skills are a must for data scientists. But to make analytics teams successful, they also need to think creatively, work in harmony and be good communicators.

  Nipa Basu • July 19, 2018

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  Hype surrounding AI technologies nears atomic proportions

  Amid the infiltration of artificial intelligence into business and society, spawning fears from some of Armageddon, humankind's greatest AI threat may very well come from within.

  Ron Karjian • Feb. 15, 2018

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  Cognitive computing in healthcare mends doctor-patient gaps

  Cognitive computing and healthcare data aggregation may prove to be important bedside companions to doctors treating their patients in the era of medical specialization.

  Scott Robinson • Feb. 15, 2018

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  AI technologies have come far, but the road is long

  Machine learning and other artificial intelligence technologies are poised to offer businesses big benefits, but companies have to walk before they run with AI.

  Bridget Botelho • Feb. 15, 2018

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