Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples
暫譯: 使用 Python 進行進階數據分析:包含機器學習、深度學習和自然語言處理範例

Sayan Mukhopadhyay

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商品描述

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. 
 
After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects.
 
What You Will Learn
  • Work with data analysis techniques such as classification, clustering, regression, and forecasting
  • Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
  • Examine the different big data frameworks, including Hadoop and Spark
  • Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP
 
Who This Book Is For
 
Data scientists and software developers interested in the field of data analytics.
 
 

商品描述(中文翻譯)

獲得廣泛的進階數據分析概念基礎,並探索最近在資料庫領域的革命,例如 Neo4j、Elasticsearch 和 MongoDB。本書討論如何實施 ETL 技術,包括主題爬蟲,這些技術應用於高頻算法交易和目標導向對話系統等領域。您還將看到機器學習概念的範例,例如半監督學習、深度學習和自然語言處理 (NLP)。使用 Python 的進階數據分析 也涵蓋了重要的傳統數據分析技術,例如時間序列和主成分分析。

閱讀本書後,您將對分析專案的每個技術面向有實際經驗。您將通過 Python 代碼了解這些概念,並獲得可用於自己專案的範例。

您將學到什麼


  • 使用分類、聚類、回歸和預測等數據分析技術

  • 處理結構化和非結構化數據、ETL 技術,以及不同類型的資料庫,如 Neo4j、Elasticsearch、MongoDB 和 MySQL

  • 檢視不同的大數據框架,包括 Hadoop 和 Spark

  • 探索進階機器學習概念,如半監督學習、深度學習和自然語言處理 (NLP)

本書適合誰閱讀

數據科學家和對數據分析領域感興趣的軟體開發人員。