禮物季第二波
Machine Learning in Action

Machine Learning in Action

  • 作者: Harrington, Peter
  • 原文出版社:Manning Publications
  • 出版日期:2012/04/19
  • 語言:英文
  • 定價:2474

分期價:(除不盡餘數於第一期收取) 分期說明

3期0利率每期8246期0利率每期412
  • 運送方式:
  • 臺灣與離島
  • 海外
  • 可配送點:台灣、蘭嶼、綠島、澎湖、金門、馬祖
  • 可配送點:台灣、蘭嶼、綠島、澎湖、金門、馬祖
載入中...
  • 分享
 

內容簡介

Summary

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

About the Book

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

What's Inside
  • A no-nonsense introduction
  • Examples showing common ML tasks
  • Everyday data analysis
  • Implementing classic algorithms like Apriori and Adaboos
Table of Contents
    PART 1 CLASSIFICATION
  1. Machine learning basics
  2. Classifying with k-Nearest Neighbors
  3. Splitting datasets one feature at a time: decision trees
  4. Classifying with probability theory: na ve Bayes
  5. Logistic regression
  6. Support vector machines
  7. Improving classification with the AdaBoost meta algorithm
  8. PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
  9. Predicting numeric values: regression
  10. Tree-based regression
  11. PART 3 UNSUPERVISED LEARNING
  12. Grouping unlabeled items using k-means clustering
  13. Association analysis with the Apriori algorithm
  14. Efficiently finding frequent itemsets with FP-growth
  15. PART 4 ADDITIONAL TOOLS
  16. Using principal component analysis to simplify data
  17. Simplifying data with the singular value decomposition
  18. Big data and MapReduce

 

作者簡介

Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.

 

詳細資料

  • ISBN:9781617290183
  • 規格:平裝 / 384頁 / 23.6 x 18.8 x 2.5 cm / 普通級
  • 出版地:美國

最近瀏覽商品

 

相關活動

  • 【自然科普、電腦資訊】張忠謀親筆撰寫、獨家授權自傳,他的一生,一場不能錯過的智慧盛宴!《張忠謀自傳》
 

購物說明

外文館商品版本:商品之書封,為出版社提供之樣本。實際出貨商品,以出版社所提供之現有版本為主。關於外文書裝訂、版本上的差異,請參考【外文書的小知識】。

調貨時間:無庫存之商品,在您完成訂單程序之後,將以空運的方式為您下單調貨。原則上約14~20個工作天可以取書(若有將延遲另行告知)。為了縮短等待的時間,建議您將外文書與其它商品分開下單,以獲得最快的取貨速度,但若是海外專案進口的外文商品,調貨時間約1~2個月。 

若您具有法人身份為常態性且大量購書者,或有特殊作業需求,建議您可洽詢「企業採購」。 

退換貨說明 

會員所購買的商品均享有到貨十天的猶豫期(含例假日)。退回之商品必須於猶豫期內寄回。 

辦理退換貨時,商品必須是全新狀態與完整包裝(請注意保持商品本體、配件、贈品、保證書、原廠包裝及所有附隨文件或資料的完整性,切勿缺漏任何配件或損毀原廠外盒)。退回商品無法回復原狀者,恐將影響退貨權益或需負擔部分費用。 

訂購本商品前請務必詳閱商品退換貨原則 

  • calendar
  • 2024
  • 禮物季第二波