SIMPLE, EFFECTIVE WAY TO ANALYZE DATA AND PREDICT OUTCOMES WITH PYTHON

Machine learning focuses on prediction?using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. Machine Learning with Spark(TM) and Python^® simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language. This edition shows how pyspark extends these two algorithms to extremely large data sets requiring multiple distributed processors. The same basic concepts apply.

Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of penalized linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves.

Designed specifically for those without a specialized math or statistics background, Machine Learning with Spark and Python shows you how to:

• Select the right algorithm for the job
• Learn the mechanisms and prepare the data
• Code demonstrates pyspark implementations scalable to big-data using hundreds of processors
• Master core Python machine learning packages
• Build versatile predictive models that work
• Apply trained models in practice for various uses
• Measure model performance for better QC and application
• Use provided sample code in Jupyter Notebook format to design and build your own model