2015
O'Reilly Media
Mikio Braun
3:08
English

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”

Part 1: Introduction
Introduction to Scalable Machine Learning 11m 12s
Some Machine Learning Background 12m 29s
Algorithms for Large Scale Learning 20m 09s
Part 2: Hadoop And Friends
Overview of Hadoop and Current Big Data Systems 14m 00s
Part 3: Programming for Data Flow Systems
How Programming for Data Flow Differs 16m 11s
Basic Spark 19m 13s
Working with Vectors and Matrices in Spark 34m 53s
A Brief Tour of Spark ML 29m 40s
Part 4: Beyond Paralleization
Approximation is the Key 15m 34s
Part 5: Practical Big Data
Practical Big Data 06m 56s
Size vs. Complexity 05m 06s
Summary 02m 53s

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