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Want to become a sentiment analysis Jedi? With the release of the Alteryx Intelligence Suite and its text mining tools, you can explore tricky text data and gain new insights into the feelings within it.
Ever wonder what activities you could do in London with a time constraint? How about how to maximize the value of your fantasy football team on a budget? Join my session at Inspire to learn how prescriptive analytics and the Optimization Tool can help you find the best solution to your most pressing questions. I’ll go into more detail on the day, but to whet your appetite, here’s a little taster of what to expect.
Today, we are very excited to announce the public preview of two Alteryx Designer tools that connect to Azure Machine Learning automated ML (Machine Learning), allowing you to easily leverage the power of automated machine learning from inside Designer.
With the Azure Machine Learning Training tool, you can send data directly from Designer to be trained by Azure Machine Learning automated ML. Use the Azure Machine Learning Scoring tool to use the model you trained for predictions on new data.
After training a Phrases model with Community texts, I wanted to be able to incorporate the model into Alteryx workflows that I was using to process text, and hopefully even be able to share the model with other Alteryx users. After thinking through this, I realized it was a perfect application for the Python SDK.
Reproducibility, the open sharing of data, and expanding on the research of others are all at the heart of the scientific process, and we live in an exciting time where it is more possible than ever. This year's Inspire Europe Closing Keynote speaker Dr. Ben Goldacre has recently published a paper examining compliance with the European Commission's guideline that all Clinical Trials registered in the EU Clinical Trials Register must report results to the European Medicines Agency within 12 months of the trial's completion. The bulk of the paper's analysis was performed in the statistical software Stata. With tools like Alteryx or Python, we have easy and open-source ways to process data and derive new knowledge. In this blog, we reproduce some of Goldacre et al.'s analysis in Alteryx and Python and provide both formats for you to further explore the data on your own.
Neural Networks are an approach to artificial intelligence that was first proposed in 1944. Modeled loosely on the human brain, Neural Networks consist of a multitude of simple processing nodes (called neurons) that are highly interconnected and send data through these network connections to estimate a target variable. In this article, I will discuss the structure and training of simple neural networks (specifically Multilayer Perceptrons, aka "vanilla neural networks"), as well as demonstrate an example neural network created by the Alteryx Neural Network Tool.
Building my first linear regression model turned me into an instant celebrity. My roommate, who has acted as a sounding board for my predictive-analytics-learning progress, now believes I can use Linear Regression to predict the winner of the next horse race. While it would be fun to try, a more applicable use case is predicting how much a customer will spend (which, in the case of horse racing could translate to how much someone might spend on a bet). For my use case, I want to predict how much a Lyft driver can expect to receive on their next fare.