Microsoft Machine Learning for Apache Spark

MMLSpark MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets. MMLSpark requires Scala 2.11, Spark 2.1+, and either…

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There’s a Visual Studio package for that! Building for Data Science In Visual Studio

Python Tools for Visual Studio Visual Studio 2017 provides rich integration for Python, covering various scenarios from machine learning to desktop to IoT to the web. It supports most interpreters such as CPython (2.x, 3.x), IronPython, Jython, PyPy, … along with the Anaconda distro and access to thousands of packages on PyPI. For the list…

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Understanding Data Science and Machine Learning

Machine Learning Tools and Architecture Cognitive Services API: Provides a broad set of API’s for vision, speech, text, knowledge and search. Azure Gallery: Find existing models you can use out of the box. Azure Machine Learning: Build powerful models using a web based drag and drop interface. Microsoft R Server: Build R models and more…

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Matrices, Data Frames, Functions, Conditionals, Loops with R

  Guest post by Slaviana Pavlovich Microsoft Student Partner My name is Slaviana Pavlovich. I am an IT and Management student at University College London with a passion for data science. I recently completed the Microsoft Professional Program for Data Science, where I developed core skills to work with data. If you are also interested…

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nbgrader to automate assessment and grading with JupyterHub on Azure Data Science VM

Jupyter notebook on Microsoft Data Science Virtual Machine The Anaconda distribution on the Microsoft Data Science VM comes with a Jupyter notebook, an environment to share code and analysis. The Jupyter notebook is accessed through JupyterHub. You sign in using your local Linux user name and password. The Jupyter notebook server has been pre-configured with…

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Getting Started with Data Science using Cortana Intelligence Solutions Templates

Cortana Intelligence Gallery Reference architectures for common data science scenarios are now available via the Cortana intelligence solution templates. These templates allow you to quickly build Data Science Solutions from solution templates which include reference architectures and design patterns. They  can be used to enhance and adapt your current models and solutions or to replicate…

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Using External data with Azure Jupyter Notebooks

One of the vital requirements for academics is to provide a single data set to allow all there students to utilise for undertaking experiments. By hosting data on a Blob Storage account you can allow students connect and undertake experiments using Azure Jupyter Notebook http://azure.notebooks.com  in a pretty straight forward manner. Data can be uploaded…

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Getting started with R · Arithmetic and logical operators, Objects, Vectors, Factors & Lists

Guest post by Slaviana Pavlovich Microsoft Student Partner I am an IT and Management student at University College London with a passion for data science. I recently completed the Microsoft Professional Program for Data Science, where I developed core skills to work with data. If you are also interested in this career, but not sure…

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Spatial Data Analysis with R

Guest blog by Jason Zhang Microsoft Student Partner at the University of Cambridge Hi, I am  Jason, a third year Natural Sciences student in University of Cambridge. With great interest in Microsoft and its work in Data Science, I joined the Microsoft Student Partner program in 2016. I then attended the Microsoft Professional Program, Data…

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How to implement the backpropagation using Python and NumPy

I was recently speaking to a University Academic and we got into the discussion of practical assessments for Data Science Students, One of the key principles students learn is how to implement the back-propagation neural network training algorithm. Many students start by learning this method from scratch, using just Python 3.x and the NumPy package….

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