Free Webinars in November – Learn from Big Data & Machine Learning Applications in Healthcare



Free Webinars in November – Learn from Big Data & Machine Learning Applications in Healthcare

Free Webinars in November – Learn from Big Data & Machine Learning Applications in Healthcare





Join us for a set of exciting webinars starting next Tuesday, November 14th, at which we’ll show you how data science and machine learning are being applied in the medical field. You will be able to take the learnings from these webinars to use Azure Machine Learning, Azure Data Lake and Hadoop Spark clusters in big data and ML solutions that are relevant to your organization or to your customers. You will also learn how technologies like ML and AI can be effectively used as a tool for cost control, which is especially critical in the healthcare industry.

More on each session below. Be sure to click the links attached to the titles of these sessions to reserve your spot today. All sessions are entirely free, of course.

Data Science and Machine Learning in Healthcare: A Population Health Management Solution

In this webinar, we talk about two things: How data science and ML can be used to manage and control escalating healthcare costs, and how to create a Population Health Management solution using state-of-the-art Azure Data Lake (ADL) Analytics. While outlining the Population Health Management solution, we will introduce you to ADL Analytics with R integration and show you how to create a real-time Population Health Report that can be visualized using Power BI.

This webinar runs from 10-11 AM Pacific Time on Tuesday, November 14th, and will be presented by Shaheen Gauher, Data Scientist at Microsoft.

Deep Learning for Biomedical Entity Extraction Using Azure ML

This webinar will demonstrate how to use the new Azure ML Workbench to solve complicated NLP tasks such as entity extraction from unstructured text. The tutorial aims to analyze a large corpus of unlabeled unstructured text records such as Medline PubMed abstracts and trains a word embedding model. The output embeddings are considered as automatically generated semantic features to train a neural entity extractor. We systematically show how to train a word embeddings model using word2vec neural word embedding algorithm with nearly 20 million Medline article abstracts on an HDInsight Spark cluster and then use the auto-generated features to train a LSTM deep recurrent neural network for medical entity extraction on a GPU-equipped Data Science Virtual Machine.

The webinar runs from 10-11 AM Pacific on Tuesday, November 21st, and will be presented by Mohamed AbdelHady, Senior Data Scientist at Microsoft.

Mark your calendars now. We look forward to having you join us at these sessions!

ML Blog Team

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