Students Will Learn to Use IBM Watson API with Help of Galvanize’s Machine Learning Course
Machine learning made easy !
What is IBM trying to do now?
IBM’s annual Inter Connect conference which is happening in Las Vegas from March 19-23, as a part of the conference IBM has announced a machine learning course in collaboration with Galvanize, a workspace and education provider. The motto behind this program is to train students regarding IBM’s suite of Watson APIs (Application Programming Interface). These APIs helps to simplify the process of creating tools which rely on language, speech and vision analysis.
How will the course help?
The course is named as IBM Cognitive Course and the class will use at least four weeks to teach the basics of machine learning and other Watson’s capabilities. Students can also take the class directly from IBM’s Bluemix cloud platform.
The CTO of IBM Watson & IBM Cloud, Bryson Koehler believes that not everyone knows what they can do with Watson API and moreover the numbers of experts in this field are few. He further states that if students have hands-on learning experience than they can become more productive developers.
Cities like Austin, Denver, New York, San Francisco, Seattle and Boston will be hosting two-day workshops for students that are known as Builders Fairs which will help the course. Builder Spaces will also host hackathons and office hours.
Galvanize and IBM’s collaboration
Galvanize’s collaboration with IBM isn’t new. Nir Kaldero, Director of Galvanize’s Data Science was awarded 2017 IBM Analytics Champion and previously they worked and trained executives on data science. The duo also worked to launch a Bluemix Academy for enterprise customers and the first Bluemix “Garage” was launched at Galvanize in San Francisco so that it can support IBM’s cloud platform.
Other major tech companies also start educating student with various such initiatives, for e.g. Google partnered up with Udacity to develop three-month course for learning deep learning techniques with the help of TensorFlow.
Do you think such kind of collaboration will help to fill the gap that persists while studying deep and machine learning ? Do share your thoughts in our comment section below.
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