MarkTab's Top Keys to Scholastic Success

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I wrote this document in the 1980s. My claim to authority is being valedictorian (shared award) at my high school (class of about 400). I attracted admittance and scholarships from some leading schools: Caltech, Harvard, MIT, Princeton

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ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more. “Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019. This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI

201906 02 Introduction to AutoML with ML.NET 1.0

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201905 Azure Databricks for Machine Learning

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Microsoft has several Azure certifications including DP-100 (Designing and Implementing a Data Science Solution on Azure). Until this month, the exam had been in beta: however, the presenter has just passed the exam (first try). The purpose of this event is to share a viewpoint on how to study for the exam. Today, there are multiple ways to develop and deliver and deploy R or Python or Spark or deep learning models on Azure. The differences are important for this exam.

201905 Azure Certification DP-100: Designing and Implementing a Data Science ...

Mark Tabladillo

This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).

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Mark Tabladillo

This presentation anchors best practices for Enterprise Data Science based on Microsoft's "Team Data Science Process". The talk includes introducing the concepts, describing some real-world advice for project planning, and discusses typical titles of professionals who make enterprise data science successful. These techniques also apply for AI (artificial intelligence), deep learning, machine learning, and advanced analytics.

Managing Enterprise Data Science 201904

Mark Tabladillo

This intermediate-level presentation covers latest Azure technology for deploying Python sci-kit models on Azure. The presentation is a demo using a Microsoft Data Science Virtual Machine (DSVM), Visual Studio Code, Azure Machine Learning Service, Azure Machine Learning Compute, Azure Storage Blobs, and Azure Container Registry to train a model from a Python 3 Anaconda environment. The presentation will include an architectural diagram and downloadable code from Github. YouTube recording at https://www.youtube.com/watch?v=HyzbxHBpAbg&feature=youtu.be

Training of Python scikit-learn models on Azure

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Big Data Adavnced Analytics on Microsoft Azure

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Power BI has become an increasingly important data analytics tool. This presentation focuses on the advanced analytics options currently available in Power BI. Attendees to this talk will see: · Microsoft’s perspective on advanced analytics development: the Team Data Science Process · What the general options are for advanced analytics on Azure · What the specific native advanced analytics capabilities are in Power BI · Some ideas on pairing Power BI with other technologies in advanced analytics architectures

Advanced Analytics with Power BI 201808

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The Microsoft Cognitive Toolkit (CNTK) is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. The objectives of this presentation is to 1) describe what CNTK is, 2) present a comparative evaluation with similar technologies, 3) outline potential applications, and 4) demonstrate the technology with Jupyter Python examples.

Microsoft Cognitive Toolkit (Atlanta Code Camp 2017)

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Machine learning services with SQL Server 2017

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Delivered to SQL Saturday BI Edition -- Atlanta, GA Microsoft provides several technologies in and around Azure which can be used for casual to serious data science. This presentation provides an overview of the major Microsoft options for both on-premise and cloud-based data science (and hybrid). These technologies have been used by the presenter in various companies and industries, both as a Microsoft consultant and previously independent consultant. As well, the speaker provides insights into data science careers, information which helps imply where the business will likely be for consultants and partners.

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Mark Tabladillo

Underneath the shiny popular apps on tablets, smartphones, and entertainment channels are typically large cloud-based data centers. App developers leverage the cloud to provide advertisers with targeted sales opportunities, which has been accounting for an ongoing shift from paper to online media. This presentation will provide updated trends and statistics for 2016 on big data usage (based on consumer use), statistical concerns with big data, and the Microsoft big data story.

How Big Companies plan to use Our Big Data 201610

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Georgia Tech Data Science Hackathon September 2016

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Delivered to SQL Saturday Columbus, GA Microsoft provides several technologies which can be used for casual to serious data science. This presentation provides an authoritative overview of two major categories: products and services. The products include: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, SQL Server R Services, Microsoft R Technologies, and F#. The services include Cortana Intelligence and Bing Predicts. These technologies have been used by the presenter in various companies and industries, and he will be speaking toward how Microsoft uses these technologies today for its largest Azure customers.

Microsoft Data Science Technologies 201608

Mark Tabladillo

Presented for the Atlanta Azure Meetup group. Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider's perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing 2) provide demos of using Microsoft R Open with AzureML 3) provide demos of Python 3 and AzureML 4) provide best practices for developing applications with Azure Machine Learning This technology is now within "Cortana Intelligence".

Insider's guide to azure machine learning 201606

Mark Tabladillo

Window functions are powerful analytic functions built into SQL Server. SQL Server 2005 introduced the core window ranking functions, and SQL Server 2012 added time and statistical percentage window functions. These functions allow for advanced variable creation, and are of direct benefit to people creating features for data science. This talk will also recommend further reading on this topic. The slide deck contains a link to the code on GitHub.

Window functions for Data Science

Mark Tabladillo

Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries.

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MarkTab's Top Keys to Scholastic Success

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