Don't miss this Big Data Analytics Meetup with talks from Nikolay Manchev and Chris Snow.
This session continues our education series of the mathematics underpinning machine learning algorithms. In this session we will do a quick refresher on basic probabilities and will cover Binary Logistic Regression. We will talk about the rules of probability and the Bernoulli distribution. We will derive the Logistic Regression model and we will look at techniques for estimating the model parameters. We will discuss certain principal assumptions of Logistic Regression, and we will see the model in action, and how it is typically used with a classification dataset.
Nikolay has over 10 years of database experience and has been involved in large scale migration, consolidation, and data warehouse deployment projects in the UK and abroad. He is a speaker, blogger, author of numerous articles and a book on advanced database topics. For the last three years Nikolay has been working exclusively in the big data (Hadoop) space with focus on Spark and machine learning. He has an M.Sc. in Software Technologies and is working towards an M.Sc. in Data Science.
In this session, we provide a high level introduction to Collaborative Filtering using Apache Spark's ALS (alternating least squares) machine learning algorithm. We then walk through an example collaborative filtering web application for movie recommendations. The application is built using services on the IBM Cloud: Data Science Experience (DSX), Spark as a Service, Cloudant, Compose Redis and Bluemix. We briefly cover each of these services and the properties they bring to the application architecture.
Chris Snow is an IBM data and application architect who loves helping customers with their data architectures. He is currently focused on IBM Cloud Data Services and emerging technologies such as big data streaming architectures.