Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python (Paperback)

Manohar Swamynathan

  • 出版商: Apress
  • 出版日期: 2017-06-07
  • 定價: $1,575
  • 售價: 5.0$788
  • 語言: 英文
  • 頁數: 358
  • 裝訂: Paperback
  • ISBN: 1484228650
  • ISBN-13: 9781484228654
  • 相關分類: Python程式語言Machine LearningData Science
  • 立即出貨(限量) (庫存=4)

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商品描述

Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. 

This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. 

You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. 

 

All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.

What You'll Learn
  • Examine the fundamentals of Python programming language
  • Review machine Learning history and evolution
  • Understand machine learning system development frameworks
  • Implement supervised/unsupervised/reinforcement learning techniques with examples
  • Explore fundamental to advanced text mining techniques
  • Implement various deep learning frameworks
 
 
Who This Book Is For
 
Python developers or data engineers looking to expand their knowledge or career into machine learning area.

 

Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.

Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.

 

商品描述(中文翻譯)

在六個步驟中使用Python來精通機器學習,並探索從基礎到高級的主題,所有這些都旨在使您成為一位有價值的從業者。

本書的方法基於“六度分隔理論”,該理論認為每個人和每件事最多只相隔六步。《在六個步驟中精通Python機器學習》將每個主題分為兩部分:理論概念和使用適當的Python套件進行實際實施。

您將學習Python編程語言的基礎知識,機器學習的歷史、演變和系統開發框架。還涵蓋了關鍵的數據挖掘/分析概念,如特徵維度降低、回歸、時間序列預測及其在Scikit-learn中的高效實現。最後,您將探索高級文本挖掘技術、神經網絡和深度學習技術及其實現。

書中提供的所有代碼將以iPython筆記本的形式提供,以便您嘗試這些示例並將其擴展為您的優勢。

您將學到什麼:

- 深入研究Python編程語言的基礎知識
- 回顧機器學習的歷史和演變
- 理解機器學習系統開發框架
- 使用示例實施監督/非監督/強化學習技術
- 探索從基礎到高級的文本挖掘技術
- 實施各種深度學習框架

適合閱讀對象:

- Python開發人員或數據工程師希望擴展他們在機器學習領域的知識或職業生涯。
- 非Python(R、SAS、SPSS、Matlab或任何其他語言)的機器學習從業者希望擴展他們在Python中的實施技能。
- 新手機器學習從業者希望學習高級主題,如超參數調整、各種集成技術、自然語言處理(NLP)、深度學習和強化學習的基礎知識。