In this tutorial, you will learn how to employ a simulated dataset from Kaggle to build a machine learning model to both predict and explain whether employees will leave their employer or not and the reason(s) why they may do so. The data comprise a wide range of topics which allow to explain employees' leave behavior in relation with A) organizational factors (department); B) employment relational factors (i.e. This tutorial has the objective to inspire you to explore the possibilities of using machine learning for your own research. You will follow several steps to explore the data and build a machine learning model to predict whether an employee will leave or not, and why. You will build this prediction model with the Azure Machine Learning Studio.

Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. Using machine learning, computers learn without being explicitly programmed. Forecasts or predictions from machine learning can make apps and devices smarter. When you shop online, machine learning helps recommend other products you might like based on what you've purchased. When your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud.

Machine learning is a technique of data science that helps computers learn from existing data in order to forecast future behaviors, outcomes, and trends. These forecasts or predictions from machine learning can make apps and devices smarter. When you shop online, machine learning helps recommend other products you might like based on what you've purchased. When your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. When your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done.

This post is authored by Sachin Chouksey, Principal Software Engineering Manager, and Darwin Schweitzer, Senior Program Manager, at Microsoft. Building analytics solutions can consume a lot of time. Customers who wish to build intelligent solutions on Microsoft's advanced analytics platform today, for instance, need to navigate through a multitude of options, thanks to the broad array of services available as part of the Cortana Intelligence Suite. This buffet of options could present a learning curve for newer customers who may not be sure where to start, or what the optimal architecture might be, or how to glue different services together. To address the above challenge, we offer Cortana Intelligence Solutions, a set of pre-built solutions that are based on commonly encountered design patterns and which customers can quickly deploy and test.

This post was authored by Rimma Nehme, Technical Assistant, Data Group at Microsoft. Today, the business potential of Machine Learning and AI is real. Businesses can apply ML and AI to transform, optimize and automate their businesses having previously relied only on human intelligence. You may wonder, 'how can I apply AI to my business?' This blog post describes some of the ways you can do that using the resources in the Cortana Intelligence Gallery, without a PhD in Machine Learning or AI or even a deep expertise in these subjects.

Last week, KDnuggets published 2 blogs by frequent contributor and Microsoft data scientist Brandon Rohrer. These blogs were transcripts of the first 2 videos in a series of "Data Science for Beginners" series featured on Microsoft's Azure website. Part 1 covered'The 5 questions data science answers,' while Part 2 touched on whether or not your data is ready for data science. The remaining 3 videos (and corresponding blog transcripts) are available now on Microsoft Azure's website, and feature the following: This video covers how to ask a sharp question, how to check whether available data is able to help answer this question, and how to properly reformulate the question if necessary. This video covers getting on with prediction. It starts with collecting data, asking a sharp question, plotting the existing data for visualization, drawing a linear model, using the model to find the answer, and creating a confidence level.

There are lots of great tools out there for building machine learning models and data processing pipelines. Most of these tools, like R, scikit-learn, spark.ml At CrowdFlower, we use many of these resources to varying degrees. However, we also recognize that many people will prefer to approach model building and deployment in a hands-on integrated environment supported by a graphical interface. To this end, we are pleased to showcase an end-to-end model construction process in Microsoft's Azure Machine Learning Studio.

Here's a task for you: Type "I want to get started with machine learning" into your favourite search engine. I get back a whole list of options from Coursera, Azure, Amazon, kdnuggets, reddit,… and I could continue on and on. So where should one get started? In this post, I want share the experiences of UK Technical Evangelist and ML Expert Amy Nicholson. Amy will share her experience of using the Cortana Intelligence Gallery in conjunction with the Azure ML Studio, and how that combination helped her break down the otherwise high barriers into popular ML techniques and start building her own ML models as well as her knowledge about this space. Amy is a graduate from The University of Sheffield in the UK, where she studied Computer Science.

This post is by Amy Nicholson, Technical Evangelist at Microsoft. Here's a task for you: Type "I want to get started with machine learning" into your favorite search engine. I get back a whole list of options from Coursera, Azure, Amazon, kdnuggets, reddit,… and I could continue on and on. So where should one get started? In this post, I share my experiences of using the Cortana Intelligence Gallery in conjunction with the Azure ML Studio, and how that combination helped me break down the otherwise high barriers into popular ML techniques and start building my own ML models as well as my knowledge about this space. A little bit of background into how I got into this project.