Hands-On Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python

Paper, David

  • 出版商: Apress
  • 出版日期: 2019-11-18
  • 售價: $1,600
  • 貴賓價: 9.5$1,520
  • 語言: 英文
  • 頁數: 242
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484253728
  • ISBN-13: 9781484253724
  • 相關分類: Python程式語言Machine LearningData Science
  • 立即出貨 (庫存=1)


Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.
All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms.
Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.

What You'll Learn

  • Work with simple and complex datasets common to Scikit-Learn
  • Manipulate data into vectors and matrices for algorithmic processing
  • Become familiar with the Anaconda distribution used in data science
  • Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction
  • Tune algorithms and find the best algorithms for each dataset
  • Load data from and save to CSV, JSON, Numpy, and Pandas formats

Who This Book Is For
The aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.


這本書可以讓有志於成為資料科學專業人士的人學習Scikit-Learn函式庫以及機器學習的基礎知識。本書結合了Anaconda Python發行版和流行的Scikit-Learn函式庫,展示了各種監督式和非監督式機器學習算法。書中通過使用Python編寫的清晰示例,詳細介紹機器學習原理,讓讀者可以在自己的電腦上進行實驗和測試。


《實戰Scikit-Learn應用於機器學習》是追求機器學習職業生涯的人的絕佳起點。閱讀本書的讀者將學習作為能力的先決條件的基礎知識。讀者將接觸到專為資料科學專業人士設計的Anaconda Python發行版,並在流行的Scikit-Learn函式庫中建立技能,該函式庫是Python世界中許多機器學習應用的基礎。

- 處理Scikit-Learn常見的簡單和複雜數據集
- 將數據轉換為向量和矩陣進行算法處理
- 熟悉用於資料科學的Anaconda發行版
- 使用分類器、回歸器和降維方法進行機器學習
- 調整算法並找到每個數據集的最佳算法
- 從CSV、JSON、Numpy和Pandas格式中加載和保存數據



Dr. David Paper is a professor at Utah State University in the Management Information Systems department. He wrote the book Web Programming for Business: PHP Object-Oriented Programming with Oracle and he has over 70 publications in refereed journals such as Organizational Research Methods, Communications of the ACM, Information & Management, Information Resource Management Journal, Communications of the AIS, Journal of Information Technology Case and Application Research, and Long Range Planning. He has also served on several editorial boards in various capacities, including associate editor. Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS, Inc., and the Phoenix Small Business Administration. He has performed IS consulting work for IBM, AT&T, Octel, Utah Department of Transportation, and the Space Dynamics Laboratory. Dr. Paper's teaching and research interests include data science, process reengineering, object-oriented programming, electronic customer relationship management, change management, e-commerce, and enterprise integration.


David Paper 博士 是猶他州立大學管理資訊系統學系的教授。他撰寫了書籍《Web Programming for Business: PHP Object-Oriented Programming with Oracle》,並在多個學術期刊上發表了70多篇論文,包括《Organizational Research Methods》、《Communications of the ACM》、《Information & Management》、《Information Resource Management Journal》、《Communications of the AIS》、《Journal of Information Technology Case and Application Research》和《Long Range Planning》。他還曾擔任多個編輯委員會的成員,包括副編輯。除了在家族企業中長大外,Paper 博士還曾在德州儀器、DLS 公司和鳳凰城小企業管理局工作。他曾為 IBM、AT&T、Octel、猶他州交通部和太空動力實驗室提供資訊系統諮詢服務。Paper 博士的教學和研究興趣包括數據科學、流程重組、物件導向程式設計、電子客戶關係管理、變革管理、電子商務和企業整合。