Large Scale Machine Learning with Python

Bastiaan Sjardin, Luca Massaron, Alberto Boschetti

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商品描述

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

About This Book

  • Design, engineer and deploy scalable machine learning solutions with the power of Python
  • Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework
  • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.

What You Will Learn

  • Apply the most scalable machine learning algorithms
  • Work with modern state-of-the-art large-scale machine learning techniques
  • Increase predictive accuracy with deep learning and scalable data-handling techniques
  • Improve your work by combining the MapReduce framework with Spark
  • Build powerful ensembles at scale
  • Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine

In Detail

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.

Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

Style and Approach

This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.

Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production.

This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

商品描述(中文翻譯)

學習快速建立強大的機器學習模型並部署大規模預測應用程式

關於本書
- 使用Python的強大功能,設計、開發和部署可擴展的機器學習解決方案
- 使用Python掌握Hadoop和Spark,實現在MapReduce框架上進行有效的機器學習
- 建立最先進的模型,開發個性化推薦系統,實現大規模機器學習

本書適合對處理大型和複雜數據集有興趣的讀者。建議讀者具備基本的Python和機器學習概念知識,並具備統計和計算數學的工作知識。

你將學到什麼
- 應用最可擴展的機器學習算法
- 使用現代化的大規模機器學習技術
- 使用深度學習和可擴展的數據處理技術提高預測準確性
- 結合MapReduce框架和Spark來改進工作效率
- 在大規模情境下建立強大的集成模型
- 使用數據流在單台機器上訓練線性和非線性預測模型,處理極大數據集

詳細內容
大型Python機器學習項目涉及與專門的機器學習架構和設計相關的新問題,許多數據科學家尚未解決。但是,尋找算法、設計和構建處理大量數據集的平台是一個日益增長的需求。數據科學家必須管理和維護越來越複雜的數據項目,隨著大數據的興起,對計算和算法效率的需求也越來越高。《Python大規模機器學習》揭示了一波滿足可擴展性需求和高預測準確性的新型機器學習算法。

深入研究可擴展機器學習和三種可擴展性形式。通過並行化和內存分配的技巧,加快可以在桌面電腦上使用的算法。熟悉專為大型項目而設計且能處理更大文件的新算法,並了解大數據環境中的機器學習。我們還將介紹在Hadoop和Spark中使用MapReduce框架的最有效的機器學習技術。

風格和方法
這本高效實用的書籍充滿了你需要確保大規模Python機器學習運行順暢的技巧、提示和工具。

大規模機器學習解決了市場上目前存在的不同問題。現在,使用Hadoop集群和在數據密集環境中工作的人可以學習從原型到生產建立強大機器學習模型的有效方法。

本書以其他語言(R、Julia、Java、Matlab)的程式設計師也能理解的方式撰寫。