This workshop is designed specifically for developers looking to integrate Machine Learning into real-world applications and to create new value from data.
It provides an agnostic introduction to operational ML with open source and cloud platforms. It is the first ML workshop to go all the way from data preparation to the integration of predictive models in real-world applications and their deployment in production. Participants will learn to use Python open source libraries scikit-learn, Pandas and SKLL, and cloud platforms Microsoft Azure ML, Amazon ML, BigML and Indico (along with their APIs).
The workshop will be given in a classroom setting with up to 20 participants.
See workshop page for more details: http://www.papis.io/workshops/
What you’re getting:
– Access to the workshop taking place on May 11 and 12, 2016 between 9.30am and 6pm at Kschool in Madrid (see address in the Registration Package)
– Resources, including slides, code and Jupyter notebooks
– A copy of Bootstrapping Machine Learning in pdf, epub and mobi formats
– May 11: Core ML
— Module 1: Introduction to Machine Learning
— Module 2: Model creation
— Module 3: Operationalization
— Module 4: Evaluation
– May 12: Going further with ML
— Module 5: Model selection
— Module 6: Data preparation
— Module 7: Advanced topics: Unsupervised Learning, Deep Learning and Recommender Systems
— Module 8: Developing your own use case
Experience in programming and with the command line
Attendees are expected to bring their own laptops for the hands-on practical work
Basic knowledge of calculus, linear algebra, and probability theory will be useful for Theory in Modules 2, 4, 5.
Members of the Big Data Spain community can enter an exclusive giveaway to get the chance to win a free ticket. Giveaway form at http://goo.gl/forms/IOTjJMZgZP Deadline May 4th at midnight. The winner will be randomly selected on May 5th.
Members of the community also get 20% off their ticket by using the following discount code “bgdtsp” on the checkout page at https://gumroad.com/l/vVvbq.