Advanced Data Analytics Using Python: With Architectural Patterns, Text and Image Classification, and Optimization Techniques

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.
Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.

Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application.

What You'll Learn

• Build intelligent systems for enterprise
• Review time series analysis, classifications, regression, and clustering
• Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning
• Use cloud platforms like GCP and AWS in data analytics
• Understand Covers design patterns in Python

Who This Book Is For

Data scientists and software developers interested in the field of data analytics.

本書介紹了使用Python進行ETL、監督式學習和PySpark等技術，深入了解時間序列和主成分分析等高級數據分析概念。書中涵蓋了數據分析中的架構模式、文本和圖像分類、優化技術、自然語言處理以及雲環境中的計算機視覺。
書中清晰解釋了Python編程中的通用設計模式，強調了架構實踐，如熱馬鈴薯反模式。您將了解Neo4j、Elasticsearch和MongoDB等數據庫的最新進展。然後，您將學習如何在圖像和文本中進行特徵工程，實現業務邏輯，並了解如何使用遷移學習構建機器學習和深度學習模型。

《Python高級分析，第2版》還介紹了使用神經網絡進行聚類、正則化技術以及強化學習中的算法設計模式。最後，書中還解釋了如何為特定應用優化模型的PySpark推薦系統。

您將學到什麼

• 為企業構建智能系統


• 回顧時間序列分析、分類、回歸和聚類


• 探索監督學習、無監督學習、強化學習和遷移學習


• 在數據分析中使用GCP和AWS等雲平台


• 了解Python中的設計模式

適合閱讀對象

對數據分析領域感興趣的數據科學家和軟件開發人員。

In his 13+ years industry experience, Sayan Mukhopadhyay has been associated with companies such as Credit-Suisse, PayPal, CA Technology, CSC, and Mphasis. He has a deep understanding of data analysis applications in domains such as investment banking, online payments, online advertising, IT infrastructure, and retail. His area of expertise is applied high-performance computing in distributed and data-driven environments such as real-time analysis and high-frequency trading.

在他超過13年的行業經驗中，Sayan Mukhopadhyay曾與Credit-Suisse、PayPal、CA Technology、CSC和Mphasis等公司合作。他對於投資銀行、線上支付、線上廣告、IT基礎設施和零售等領域的數據分析應用有深入的了解。他的專業領域是應用於分散和數據驅動環境中的高性能計算，例如實時分析和高頻交易。