Mastering Machine Learning with Spark 2.x

1. Mastering Machine Learning with Spark 2.x
   
2. Alex Tellez, Max Pumperla, Michal Malohlava
   
3. August 2017
   
4. Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial

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1. Book Description
   
2. 
   
3. The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter.
   
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5. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification.
   
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7. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
   
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9. Table of Contents
   
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11. 1: INTRODUCTION TO LARGE-SCALE MACHINE LEARNING AND SPARK
    
12. 2: DETECTING DARK MATTER - THE HIGGS-BOSON PARTICLE
    
13. 3: ENSEMBLE METHODS FOR MULTI-CLASS CLASSIFICATION
    
14. 4: PREDICTING MOVIE REVIEWS USING NLP AND SPARK STREAMING
    
15. 5: WORD2VEC FOR PREDICTION AND CLUSTERING
    
16. 6: EXTRACTING PATTERNS FROM CLICKSTREAM DATA
    
17. 7: GRAPH ANALYTICS WITH GRAPHX
    
18. 8: LENDING CLUB LOAN PREDICTION
    
19. What You Will Learn
    
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21. Use Spark streams to cluster tweets online
    
22. Run the PageRank algorithm to compute user influence
    
23. Perform complex manipulation of DataFrames using Spark
    
24. Define Spark pipelines to compose individual data transformations
    
25. Utilize generated models for off-line/on-line prediction
    
26. Transfer the learning from an ensemble to a simpler Neural Network
    
27. Understand basic graph properties and important graph operations
    
28. Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language
    
29. Use K-means algorithm to cluster movie reviews dataset
    
30. Authors
    
31. 
    
32. Alex Tellez
    
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34. Alex Tellez is a life-long data hacker/enthusiast with a passion for data science and its application to business problems. He has a wealth of experience working across multiple industries, including banking, health care, online dating, human resources, and online gaming. Alex has also given multiple talks at various AI/machine learning conferences, in addition to lectures at universities about neural networks. When he’s not neck-deep in a textbook, Alex enjoys spending time with family, riding bikes, and utilizing machine learning to feed his French wine curiosity!
    
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36. Max Pumperla
    
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38. Max Pumperla is a data scientist and engineer specializing in deep learning and its applications. He currently works as a deep learning engineer at Skymind and is a co-founder of aetros.com. Max is the author and maintainer of several Python packages, including elephas, a distributed deep learning library using Spark. His open source footprint includes contributions to many popular machine learning libraries, such as keras, deeplearning4j, and hyperopt. He holds a PhD in algebraic geometry from the University of Hamburg.

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Lisrelchen 发表于 2017-9-25 02:42

nice, thanks for share.

哪里有下载？