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Azure ML Thursday 7: xgboost in Azure ML Studio

Last week, we trained an xgboost model for our dataset inside R. In order to use your trained dataset in Azure ML, you need to export & upload it much like we did two weeks ago in Python. Today, I’ll show how to import the trained R model into Azure ML studio, thus enabling you[...]
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Azure ML Thursday 6: xgboost in R

Last Azure ML Thursdays we explored how to do our Machine Learning in Python. Python in Azure ML doesn’t include one particularly succesful algorithm though – xgboost. Python packages are available, but just not yet for Windows – which means also not inside Azure ML Studio. But they are available inside R! Today, we take the same approach[...]
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Azure ML Thursday 4: ML in Python

On this fourth Azure ML Thursday series we move our ML solution out of Azure ML and set our first steps in Python with scikit-learn. Today, we look at using “just” Python for doing ML, next week we bring the trained models to Azure ML. You’ll notice there’s a lot more to tweak and improve once you do[...]
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Azure ML Thursday 2: Train, Test, Submit!

On this second Azure ML Thursday, I’ll discuss a first entry on a competition. Also, some background about splits and cross-validation. Microsoft has provided a walkthrough for your first entry, so I won’t describe all the steps you’ll need to take. Rather, I’ll provide some first, easy tweaks to the first submission.
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Azure ML Thursday

Past two weeks, I’ve explored some applications of Machine Learning by doing a Cortana Intelligence competition (https://gallery.cortanaintelligence.com/competitions). This is a Kaggle-like challenge, but restricted to the Azure ML environment, which creates some challenges of its own. Because the deadline of the challenge is on October 10th, I cannot post all my experiences (yet). So I’ve[...]
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