A Day of Machine Learning from IBM @ Galvanize

It was a cold day in San Francisco when a group of eager students gathered for an all-day Women in Big Data Machine Learning training sponsored by IBM @ Galvanize. Thanks to IBM’s Nick Dimtchey and Karmen Leung for sponsoring the event and facilitating the connection with Galvanize. The topic was timely given today’s hot market, and we believe that this Machine Learning training at Galvanize was an excellent step toward getting more women in Big Data jobs! Many thanks also to Andrew Fitch at Galvanize for providing the awesome space, smart instructors and yummy food!

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Despite a cold winter morning, it was full house, with 80% attendance by women and 20% representation by men. The day started with breakfast, informal networking and Cynthia Kaschub kicking off the WiBD event by explaining the vision and strategy for WiBD—including a call to action for attendees to get involved by attending events, sponsoring events or volunteering on the Training, Mentoring, Networking, or Awareness & Evangelism committees).

After Cynthia kicked off the event, she introduced IBM’s Global Head of Analytics Open Source Program office, Nick Dimtchev. Nick stressed the value of the Big Data community and the important role that events like this play in developing not only big data but industry more broadly. He also noted that IBM has additional training available through their Big Data University initiative, where interested professionals can choose suitable tracks and earn badges to build careers in Big Data. Nick also announced that IBM will continue to support Women in Big Data with four additional events in 2017.

After Nick, Andrew Fitch, an evangelist from Galvanize, highlighted Galvanize’s mission and offerings for its students and community members.

Galvanize University professor Jared Thompson and Galvanize senior instructors Cary Goltermann and Chris Overton delivered a highly interactive, hands-on training, taking all questions as they came. It was a hands-on experience handling Big Data with Spark and building neural networks with TensorFlow. They were especially good at getting the audience members to interact with each other.

The Spark content included an introduction to Spark’s basic concepts and architecture, followed by a discussion of basic Spark syntax for developing solutions. In the afternoon, the class focused on learning Spark SQL for querying big data in a structured format, followed by an introduction to artificial neurons and neural networks, using pseudologistic regression as a learning case. Later the focus switched to TensorFlow as an example of a standard, modern neural net framework useful for complex networks. Throughout, content was understandable by beginners, but experienced professionals had challenging tutorials to work on too. The hands-on nature of the course helped attendees attain knowledge they could feel confident in applying in the real-world.

Finally, Jared hosted a session on Artificial Neural Networks for folks interested in learning about the exciting potential applications of these technologies.

The best way for students to build a home installation of Spark is posted here. To check out the training materials, please click here.

Thanks once again to IBM & Galvanize for hosting Women in Big Data. We are looking forward to attending future IBM-sponsored events at Galvanize in 2017!

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