Database Systems: Design, Implementation, & Management 12th Edition, ISBN-13: 978-1305627482 
Computer Science Illuminated 7th Edition by Nell Dale, ISBN-13: 978-1284155617 
Design Of Analog CMOS Integrated Circuit 2nd Edition, ISBN-13: 978-0072524932 
C# 10 in a Nutshell: The Definitive Reference 1st Edition by Joseph Albahari, ISBN-13: 978-1098121952 
Artificial Intelligence: With an Introduction to Machine Learning 2nd Edition, ISBN-13: 978-1138502383 
Effective Java 3rd Edition Joshua Bloch, ISBN-13: 978-0134685991 
Foundations of Algorithms 5th Edition, ISBN-13: 978-1284049190 
Concrete Mathematics: A Foundation for Computer Science 2nd Edition, ISBN-13: 978-0201558029 
Cyber Threat Intelligence, Advances in Information Security 2018 Edition, ISBN-13: 978-3319739502 
Computer Networking: A Top Down Approach 8th edition James Kurose, ISBN-13: 978-0135928608 
Programming Multicore and Many-core Computing Systems, ISBN-13: 978-0470936900 
Operating System Concepts 9th Edition Abraham Silberschatz, ISBN-13: 978-1118063330 
Deep Learning (Adaptive Computation and Machine Learning series), ISBN-13: 978-0262035613 
Ethics in Information Technology 6th Edition George W. Reynolds, ISBN-13: 978-1337405874 
Becoming a Graphic and Digital Designer: A Guide to Careers in Design, ISBN-13: 978-1118771983 
Starting Out with Python 4th GLOBAL Edition, ISBN-13: 978-1292225753 
Psychiatric & Mental Health Nursing for Canadian Practice (4th edition) – eBook PDF 
Business Data Networks and Security 11th Edition, ISBN-13: 978-0134817125 
Java Foundations: Introduction to Program Design and Data Structures 5th Edition John Lewis, ISBN-13: 978-0135205976 
C# in Depth: Fourth Edition 4th Edition by Jon Skeet, ISBN-13: 978-1617294532 
Beginning Algorithms by Simon Harris and James Ross, ISBN-13: 978-0764596742 
Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence, ISBN-13: 978-3319730035 
Enhanced Discovering Computers 2017 Enhanced Edition Misty E. Vermaat, ISBN-13: 978-1305657458 
Big Data Analysis with Python: Combine Spark and Python by Ivan Marin, ISBN-13: 978-1789955286 
Auditing IT Infrastructures for Compliance 2nd Edition, ISBN-13: 978-1284090703 
Digital Image Processing 4th Edition by Rafael Gonzalez, ISBN-13: 978-0133356724 
Data Structures and Algorithms in C++ 4th Edition, ISBN-13: 978-1133608424 
Security Strategies in Windows Platforms and Applications 3rd Edition by Michael G. Solomon, ISBN-13: 978-1284175622 
Artificial Intelligence: A Modern Approach 3rd Edition by Stuart Russell, ISBN-13: 978-0136042594 
Programming Logic and Design, Comprehensive by Joyce Farrell, ISBN-13: 978-1337102070 
Microelectronic Circuits 8th Edition Adel S. Sedra, ISBN-13: 978-0190853464 
Getting to Know ArcGIS Desktop 5th Edition Michael Law, ISBN-13: 978-1589485105 
Managing Risk in Information Systems 3rd Edition by Darril Gibson, ISBN-13: 978-1284183719 
Digital Control System Analysis & Design 4th Global Edition, ISBN-13: 978-1292061221 
CISSP Cert Guide 3rd Edition by Robin Abernathy, ISBN-13: 978-0789759696 
Information Technology Project Management 5th Edition, ISBN-13: 978-1118911013 
Description
Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics 2nd Edition, ISBN-13: 978-1119561934
[PDF eBook eTextbook]
- Publisher: Wiley; 2nd edition (November 5, 2019)
- Language: English
- 368 pages
- ISBN-10: 1119561930
- ISBN-13: 978-1119561934
Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark―a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.
Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
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™ 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
MICHAEL BOWLES teaches machine learning at UC Berkeley, University of New Haven and Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as semi conductor inspection, drug design and optimization and trading in the financial markets. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses are always popular and receive great feedback from participants.
What makes us different?
• Instant Download
• Always Competitive Pricing
• 100% Privacy
• FREE Sample Available
• 24-7 LIVE Customer Support
Victoria Hughes (verified owner) –
Immediate delivery and easy checkout process.
Gavin Carter (verified owner) –
Very smooth transaction, quick delivery.