Mastering Machine Learning with Spark

Alex Tellez, Max Pumperla, Michal Malohlava

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商品描述

Key Features

  • Process and analyze big data in a distributed and scalable way
  • Write sophisticated Spark pipelines that incorporate elaborate extraction
  • Build and use regression models to predict flight delays

Book Description

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.

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.

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.

What you will learn

  • Use Spark streams to cluster tweets online
  • Run the PageRank algorithm to compute user influence
  • Perform complex manipulation of DataFrames using Spark
  • Define Spark pipelines to compose individual data transformations
  • Utilize generated models for off-line/on-line prediction
  • Transfer the learning from an ensemble to a simpler Neural Network

商品描述(中文翻譯)

《機器學習與Apache Spark》

主要特點



  • 以分散和可擴展的方式處理和分析大數據

  • 編寫複雜的Spark流程,包括精細的數據提取

  • 建立和使用回歸模型來預測航班延誤

書籍描述


機器學習的目的是建立能夠從數據中學習的系統。能夠理解複雜數據中的趨勢和模式對於成功至關重要;這是當今具有挑戰性的市場實現增長的關鍵策略之一。隨著機器學習的迅猛發展,開發人員現在渴望了解如何使他們的Spark應用程序更加智能。


本書讓您能夠將數據轉化為可行的知識。本書首先通過MLlib和H2O庫定義機器學習基本原理。您將學習如何使用二元分類在由CERN粒子對撞機產生的大量數據中檢測希格斯玻色子粒子,並使用集成方法進行多類別分類以分類日常健康活動。


接下來,您將解決一個典型的回歸問題,涉及航班延誤預測並編寫複雜的Spark流程。您將使用doc2vec算法和K-means聚類分析Twitter數據。最後,您將使用MLlib構建不同的模式挖掘模型,使用Spark和Spark SQL進行複雜的DataFrame操作,並在Spark流式處理環境中部署應用程序。

您將學到什麼



  • 使用Spark流式處理在線聚類推文

  • 運行PageRank算法計算用戶影響力

  • 使用Spark進行複雜的DataFrame操作

  • 定義Spark流程以組合個別的數據轉換

  • 利用生成的模型進行離線/在線預測

  • 將從集成學習轉移到更簡單的神經網絡中的學習