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DTSTAMP:20260518T054033Z
DESCRIPTION:Click for Latest Location Information: http://edw2019.dataversi
 ty.net/sessionPop.cfm?confid=126&proposalid=10543\n<div>\n<p>NOTE: This sem
 inar continues to Friday morning.</p>\n<div>No organization should hesitate
  to deploy Artificial Intelligence (AI) and Machine Learning (ML) throughou
 t their company and products. On the contrary, a failure to do so will cert
 ainly imply obsolescence.&nbsp;In fact, today&rsquo;s current open-source s
 oftware environment makes it easier than ever to quickly prototype products
  and experiments to easily see and iterate on the potential productivity ga
 ins and benefits that ML can have on your company&rsquo;s work and impact, 
 whether internal to the organization&#39;s efficiency or external through y
 our company&rsquo;s core product value.<br />\n&nbsp;</div>\n</div>\n<div>T
 his two-part workshop will bring participants up to speed on the complete D
 ata Science spectrum: from data munging and cleaning to data exploration an
 d visualizations, to building machine learning models and predictive analyt
 ics, with a focus on ML models and their applications.<br />\n&nbsp;</div>\
 n<div>Within the Jupyter notebook universe, using both Python and R program
 ming languages and packages, we will build and explore working code on vary
 ing datasets that cover a breadth of topics and contexts, motivating the in
 troduction of clustering techniques, classification problems, and regressio
 n: the overarching classes of problems into which all data problems fit.&nb
 sp;We will go over the basic ideas of the various techniques as well as the
  motivations for their use: when to use them and why.<br />\n&nbsp;</div>\n
 <div>We will utilize K-Nearest Neighbors algorithms (KNN) and Principle Com
 ponents Analysis (PCA) for dimensionality reduction and clustering, Logisti
 c Regression and Random Forests for classification, and Neural Networks and
  extreme Gradient Boosted Decision Trees (xgBoost) for regression and predi
 ctive modeling.&nbsp;We will also briefly extend the models to include mode
 rn Deep Learning motivations and uses.<br />\n&nbsp;</div>\n<div>Participan
 ts will leave with a solid cursory understanding of the types of problems t
 hat Artificial Intelligence and Machine Learning can tackle, as well as the
  practical skills and know-how to bring the approaches back to their respec
 tive industries and problems.&nbsp;Each participant will leave with meaning
 ful, working code.&nbsp;More importantly, however, each participant will le
 ave with the mindset and optimism of using machine learning to approach and
  transform any data problem across their individual industries and settings
 .</div>\n
DTSTART:20190321T141500
SUMMARY:Getting Hands-On with Machine Learning
DTEND:20190321T172959
LOCATION: See Description
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