
InfiniteSkills – Scalable Machine Learning Training video tutorial
English | Size: 812.49 MB (851,961,016 Bytes)
Category: Tutorial
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
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