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DTSTAMP:20260511T032455Z
DESCRIPTION:Click for Latest Location Information: http://edw2018.dataversi
 ty.net/sessionPop.cfm?confid=121&proposalid=9764\n<div style="margin:0;">\n
 <p><strong>NOTE: This seminar continues from Thursday afternoon.&nbsp;</str
 ong></p>\n<p>No organization should hesitate to deploy Artificial Intellige
 nce (AI) and Machine Learning (ML) throughout their company and products. O
 n the contrary, a failure to do so will certainly imply obsolescence. In fa
 ct, today&rsquo;s current open-source software environment makes it easier 
 than ever to quickly prototype products and experiments to easily see and i
 terate 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 val
 ue.</p>\n<p>This two-part workshop will bring participants up to speed on t
 he complete Data Science spectrum: from data munging and cleaning to data e
 xploration and visualizations, to building machine learning models and pred
 ictive analytics, with a focus on ML models and their applications.</p>\n<p
 >Within the Jupyter notebook universe, using both Python and R programming 
 languages and packages, we will build and explore working code on varying d
 atasets that cover a breadth of topics and contexts, motivating the introdu
 ction of clustering techniques, classification problems, and regression: th
 e overarching classes of problems into which all data problems fit. We will
  go over the basic ideas of the various techniques as well as the motivatio
 ns for their use: when to use them and why.</p>\n<p>We will utilize K-Neare
 st Neighbors algorithms (KNN) and Principle Components Analysis (PCA) for d
 imensionality reduction and clustering, Logistic Regression and Random Fore
 sts for classification, and Neural Networks and extreme Gradient Boosted De
 cision Trees (xgBoost) for regression and predictive modeling. We will also
  briefly extend the models to include modern Deep Learning motivations and 
 uses.</p>\n<p>Participants will leave with a solid cursory understanding of
  the types of problems that Artificial Intelligence and Machine Learning ca
 n tackle, as well as the practical skills and know-how to bring the approac
 hes back to their respective industries and problems. Each participant will
  leave with meaningful, working code. More importantly, however, each parti
 cipant will leave with the mindset and optimism of using machine learning t
 o approach and transform any data problem across their individual industrie
 s and settings.</p>\n</div>\n
DTSTART:20180427T083000
SUMMARY:Getting Hands-On with Machine Learning
DTEND:20180427T114459
LOCATION: See Description
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