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2016 Phoenix Data Conference

The Third Phoenix Data Conference – the largest big data event in the Phoenix Valley had a tremendous response this year. Having established itself as a must attend big data event in the Phoenix area, the event this year attracted thought leaders and key companies discussing latest trends and technologies in the big data space. Over 350 technologists, business leaders, data analysts and engineers attended the 2016 conference The Phoenix data conference 2016, that concluded last Saturday focussed on practical big data use cases from leading analytics companies like Cloudera, SAP, Clairvoyant, MapR, StreamSets, Microsoft, Confluent, SnapLogic, DataTorrent, Tresata, Amazon, Choice Hotels, MemSQL, Wells Fargo and others. Technology leaders in the big data space shared innovative implementations and advances in the hadoop space. Specific challenges around security, talent availability, technical deployments, managed services etc were discussed by the speakers. We would like to thank our speakers, sponsors, all the attendees and the volunteers for making the Phoenix Data Conference – 2016 a huge success….

Analytics

Intro to Machine Learning

  (kdnuggets.com)   Machine Learning, Statistical modeling and Predictive Analytics have been around for a long time but the hype is right now due to rise of social media in the last decade. Tera bytes and Peta bytes of digital data is being created on a daily basis. This data contains a lot of hidden stories and insights, implementing a business feature or solution using those can be beneficial to its owners. But it is impossible for humans to read through that much data manually and derive insights. This is where machine learning comes in handy. Ok, so what is Machine Learning? In simple technical terms, it is training a statistical algorithm/model with historic data and using that model to predict outcome of new or unseen data. Machine Learning can be categorized into 2 high level groups. Supervised Learning Unsupervised Learning. Supervised Learning In supervised learning, each entry in dataset…