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Machine Learning: How to Make it Work in Your Organization [Live Roundtable]

Machine learning is a critical tool used for gaining actionable insight into ever increasing amounts of data. In this roundtable moderated by Ismail Parsa of Amazon, experts from Twitter, Skytree, Uber, and Adconion will discuss how to apply machine learning to practical problems in real organizations.

Join this live roundtable to ask these experts questions on everything from how to build a data science team to how to assess business needs and choose the appropriate ML model.
Recorded Jul 25 2013 64 mins
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Presented by
Ismail Parsa, Amazon; Gabor Szabo, Twitter; Patrick Giordani, Adconion; Alexander Gray, Skytree
Presentation preview: Machine Learning: How to Make it Work in Your Organization [Live Roundtable]

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  • The Coming Machine Learning Wave: 4 Steps to Avoid Getting Left Behind Recorded: Sep 12 2013 46 mins
    Alexander Gray, PhD, CTO and Co-Founder, Skytree
    Organizations of all kinds are now seeking strong machine learning (ML) capabilities to stay competitive. Should they build or buy, and what does it depend on? What is the likely ML expertise obtainable? Where should it live organizationally? How mature are available ML tools, and what is their true ownership cost?

    In this webinar, we will describe five critical steps of developing machine capabilities, determine the true cost of ownership of machine learning tools, outline how successful organizations have set up their data science capabilities organizationally, and pinpoint a spectrum of hybrid strategies for creating in-house machine learning capabilities spanning the build vs. buy extremes.
  • Machine Learning: How to Make it Work in Your Organization [Live Roundtable] Recorded: Jul 25 2013 64 mins
    Ismail Parsa, Amazon; Gabor Szabo, Twitter; Patrick Giordani, Adconion; Alexander Gray, Skytree
    Machine learning is a critical tool used for gaining actionable insight into ever increasing amounts of data. In this roundtable moderated by Ismail Parsa of Amazon, experts from Twitter, Skytree, Uber, and Adconion will discuss how to apply machine learning to practical problems in real organizations.

    Join this live roundtable to ask these experts questions on everything from how to build a data science team to how to assess business needs and choose the appropriate ML model.
  • Choosing the Right Machine Learning Methods Recorded: Apr 18 2013 73 mins
    Alexander Gray PhD, CTO & Co-Founder at Skytree, Inc.
    In this webinar we cover pragmatic ways to select the right machine learning/advanced analytics methods. We’ll discuss:
    –Tradeoffs between accuracy, speed, and simplicity
    –Approaches to classification, regression, density estimation, dimension reduction, clustering, and multidimensional querying
    –How to manage the tradeoffs for each type of task

    Who should attend:
    Data Scientists who are currently facing challenging problems and would like to know where to best focus their energies, or who would like to expand their technical intuition for machine learning tools in general.

    Why this topic?
    Machine learning has powerful capabilities––but is a complex field consisting of an avalanche of research papers every year. These papers occur under various headings including multivariate statistics, pattern recognition, and data mining, and span over decades. Given specific dataset(s) and analytics goals, how does one navigate the zoo of available methods, of which there are thousands of variants proposed every year?
    Is logistic regression the answer to everything? Support vector machines? Random forests? Neural networks?
    To complicate the problem of finding such answers, most textbooks are written for researchers rather than practitioners, and the few attempts at practical guides are not supported by the depth of expertise which is typical of textbooks. Further adding to the confusion, each type of method has its vocal proponents, often with limited knowledge of other methods and unstated subjective leanings, and objective comparisons between methods are challenging to perform and thus difficult to find or trust.
    In this webinar we will summarize some practical rules of thumb derived from two decades of both applied and theoretical work on over 100 challenging real-world data analysis problems from a wide span of industries and sciences.
  • Using Machine Learning to Find New Customers Recorded: Apr 17 2013 42 mins
    Paul Salazar, VP of Sales at Skytree
    We explore how Advanced Analytics & Machine Learning can be used to find and score potential prospects with better accuracy and improved conversion rates. This applies in particular to massive datasets, where the basic analytics of the past are no longer effective.
    Learn how ML can be used for advanced lead scoring and segmentation.
    Hear about how you can increase customer conversion rates.
    See a live demo of our exciting new Find New Customers application.

    Who should watch:
    CMO’s, VP of Sales (Operations), and other leaders in marketing and business strategy should attend. Learn how to unleash the power of advanced analytics/machine learning to accelerate business.
The Machine Learning Company
Skytree’s Machine Learning platform gives organizations the power to discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets and customers. Predictive Analytics and Machine Learning are quickly becoming must-have technologies in the age of Big Data, and Skytree provides the Enterprise-grade foundation. Skytree’s flagship product—Skytree Server—is the only general purpose scalable Machine Learning system on the market, built for the highest accuracy at unprecedented speed and scale.

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  • Title: Machine Learning: How to Make it Work in Your Organization [Live Roundtable]
  • Live at: Jul 25 2013 6:00 pm
  • Presented by: Ismail Parsa, Amazon; Gabor Szabo, Twitter; Patrick Giordani, Adconion; Alexander Gray, Skytree
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