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DTSTAMP:20260511T031948Z
DESCRIPTION:Click for Latest Location Information: http://edw2018.dataversi
 ty.net/sessionPop.cfm?confid=121&proposalid=9749\n<div style="margin:0;">\n
 <p><strong>NOTE: This seminar continues to Friday morning.</strong></p>\n<d
 iv>No organization should hesitate to deploy Artificial Intelligence (AI) a
 nd Machine Learning (ML) throughout their company and products. On the cont
 rary, a failure to do so will certainly imply obsolescence.&nbsp;In fact, t
 oday&rsquo;s current open-source software environment makes it easier than 
 ever to quickly prototype products and experiments to easily see and iterat
 e on the potential productivity 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.<b
 r />\n&nbsp;</div>\n</div>\n<div>This two-part workshop will bring particip
 ants up to speed on the complete Data Science spectrum: from data munging a
 nd cleaning to data exploration and visualizations, to building machine lea
 rning models and predictive analytics, with a focus on ML models and their 
 applications.<br />\n&nbsp;</div>\n<div>Within the Jupyter notebook univers
 e, using both Python and R programming languages and packages, we will buil
 d and explore working code on varying datasets that cover a breadth of topi
 cs and contexts, motivating the introduction of clustering techniques, clas
 sification problems, and regression: the overarching classes of problems in
 to which all data problems fit.&nbsp;We will go over the basic ideas of the
  various techniques as well as the motivations for their use: when to use t
 hem and why.<br />\n&nbsp;</div>\n<div>We will utilize K-Nearest Neighbors 
 algorithms (KNN) and Principle Components Analysis (PCA) for dimensionality
  reduction and clustering, Logistic Regression and Random Forests for class
 ification, and Neural Networks and extreme Gradient Boosted Decision Trees 
 (xgBoost) for regression and predictive modeling.&nbsp;We will also briefly
  extend the models to include modern Deep Learning motivations and uses.<br
  />\n&nbsp;</div>\n<div>Participants will leave with a solid cursory unders
 tanding of the types of problems that Artificial Intelligence and Machine L
 earning can tackle, as well as the practical skills and know-how to bring t
 he approaches back to their respective industries and problems.&nbsp;Each p
 articipant will leave with meaningful, working code.&nbsp;More importantly,
  however, each participant will leave with the mindset and optimism of usin
 g machine learning to approach and transform any data problem across their 
 individual industries and settings.</div>\n
DTSTART:20180426T141500
SUMMARY:Getting Hands-On with Machine Learning
DTEND:20180426T172959
LOCATION: See Description
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