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.
Table of Contents
- Part 1: Introduction
- Part 2: Hadoop And Friends
- Part 3: Programming for Data Flow Systems
Part 4: Beyond Paralleization
- Approximation is the Key 00:15:34
- Part 5: Practical Big Data
- Title: Scalable Machine Learning
- Release date: December 2015
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491939437