Machine-learning expert Mikio Braun moves budding data scientists into the world of big data with this overview of how to do complex data analysis at scale. You'll learn the general concepts behind machine learning, compare small scale and large scale data analysis algorithms, and review the basics of the architectures used in large-scale distributed processing. You'll then explore the use of Spark programming for data flow systems,and the many uses of approximation. Braun also outlines evaluation, feature extraction, and model-selection computing costs in big data analysis. The video closes with a discussion of the relationship between the amount of available data and the complexity of the learning problem.
Review machine learning concepts such as fitting a model to data
Learn core concepts behind large scale algorithms like stochastic gradient descent
Review the architectures used in Hadoop-based systems and data flow systems
Explore resilient distributed dataset structures, vectors, and matrices using Spark
Review Sparks’s machine libraries and how to run basic machine learning tasks
Understand the use of approximation in optimization and compressing feature spaces
Learn what makes data “complex”
Mikio Braun is a data scientist researcher, a start-up entrepreneur, and the on-going creator of jblas, the open source library for fast linear algebra in Java. He has a Ph.D. in Computer Science, and works at Zalando.
Mikio Braun is co-founder of streamdrill, a startup focused on approximative approaches for real-time big data, and post-doc researcher at TU Berlin, Germany. He holds a Ph.D. in Machine Learning and has worked in research for a number of years, before becoming interested in putting research results into good use in the industry. His current interests focus on anything to do with real-time data analysis, in particular using approximative approaches beyond scaling.