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Sander Timmer
9,443 followers -
Microsoft Data Scientist | PhD | University of Cambridge alumni
Microsoft Data Scientist | PhD | University of Cambridge alumni

9,443 followers
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Sander's posts

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The emerging technology hype cycle by Gartner for 2016 covers 3 key Technologies trends:

Transparently immersive experiences: Technology will continue to become more human-centric to the point where it will introduce transparency between people, businesses and things. This relationship will become much more entwined as the evolution of technology becomes more adaptive, contextual and fluid within the workplace, at home, and interacting with businesses and other people.

The perceptual smart machine age: Smart machine technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks that will allow organizations with smart machine technologies to harness data in order to adapt to new situations and solve problems that no one has encountered previously.

The platform revolution: Emerging technologies are revolutionizing the concepts of how platforms are defined and used. The shift from technical infrastructure to ecosystem-enabling platforms is laying the foundations for entirely new business models that are forming the bridge between humans and technology.

http://www.gartner.com/newsroom/id/3412017
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Evaluating Machine Learning models when dealing with imbalanced classes

In this blog post I talk through an example of how to pick the best model when you deal with these kind of problems. I also touch the subject of cost-sensitive predictions, introducing some code to generate plots that will help you understand your model in cost fashion. Even more important, it will be essential for grasping the full business impact when moving to a data driven world!

#DataScience #R #MachineLearning #AzureML 

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Sharing of code on Github for Azure Machine Learning examples

From now one, for each of my blog posts I will document the code in a more useful way by putting it into a Github repository. Hope this helps as the current way of sharing code through MSDN blogs is not easy to replicate. 

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Using Azure Machine Learning Notebooks for Quality Control of Automated Predictive Pipelines

When building an automated predictive pipeline, to have periodically batch-wise score new data, there is a need to control for quality of the predictions. The Azure Data Factory (ADF) pipeline will help you ensure that your whole data set gets scored. However, this is not taking into consideration that data can change over time. For example, when predicting churn changes in your website or service offerings could change customer behavior in such a way that retraining of the original model is needed. In this blog post I show how you can use #Jupyter Notebooks in +Microsoft Azure Machine Learning (AML) to get a more systematic view on the (predictive) performance of your automated predictive pipelines.

#DataScience #MachineLearning #Azure #AzureDataFactory #Python #Notebook

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So Sunday is my most productive day... hmm! Guess this is due to the shopping list that I tick off during the weekend!
#Wunderlist   #yearinreview  

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Hotel room with a view on downtown Boston, Charles river, and Fenway Park. 
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Found this amazing infographic showing the wide variety of jobs that exist in the data science industry. 

http://www.sandertimmer.nl/2015/11/the-data-science-industry-jobs/

#DataScience   #jobs   #np  

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Autumn in Boston 
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SAIL Amsterdam is a quinquennial maritime event in Amsterdam in the Netherlands. Tall ships from all over the world visit the city to moor in its eastern harbour, and people can then visit the ships.

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