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DTSTAMP:20260420T132440Z
DESCRIPTION:Click for Latest Location Information: http://edw2019.dataversi
 ty.net/sessionPop.cfm?confid=126&proposalid=10545\n<div>\n<p>NOTE: This sem
 inar is continued from Thursday afternoon.</p>\n<div>No organization should
  hesitate to deploy Artificial Intelligence (AI) and Machine Learning (ML) 
 throughout their company and products. On the contrary, a failure to do so 
 will certainly imply obsolescence.&nbsp;In fact, today&rsquo;s current open
 -source software environment makes it easier than ever to quickly prototype
  products and experiments to easily see and iterate on the potential produc
 tivity gains 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 your company&rsquo;s core product value.<br />\n&nbsp;</div>\n</div
 >\n<div>This two-part workshop will bring participants up to speed on the c
 omplete Data Science spectrum: from data munging and cleaning to data explo
 ration and visualizations, to building machine learning models and predicti
 ve analytics, with a focus on ML models and their applications.<br />\n&nbs
 p;</div>\n<div>Within the Jupyter notebook universe, using both Python and 
 R programming languages and packages, we will build and explore working cod
 e on varying datasets that cover a breadth of topics and contexts, motivati
 ng the introduction of clustering techniques, classification problems, and 
 regression: the overarching classes of problems into which all data problem
 s fit.&nbsp;We will go over the basic ideas of the various techniques as we
 ll 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 Prin
 ciple Components Analysis (PCA) for dimensionality reduction and clustering
 , Logistic Regression and Random Forests for classification, and Neural Net
 works and extreme Gradient Boosted Decision Trees (xgBoost) for regression 
 and predictive modeling.&nbsp;We will also briefly extend the models to inc
 lude modern Deep Learning motivations and uses.<br />\n&nbsp;</div>\n<div>P
 articipants will leave with a solid cursory understanding of the types of p
 roblems that Artificial Intelligence and Machine Learning can tackle, as we
 ll as the practical skills and know-how to bring the approaches back to the
 ir respective industries and problems.&nbsp;Each participant will leave wit
 h meaningful, working code.&nbsp;More importantly, however, each participan
 t will leave with the mindset and optimism of using machine learning to app
 roach and transform any data problem across their individual industries and
  settings.</div>\n
DTSTART:20190322T083000
SUMMARY:Getting Hands-On With Machine Learning 
DTEND:20190322T114459
LOCATION: See Description
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