Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started
by Drew Conway and John Myles White
pages 页数: 324 pages
Publisher Finelybook 出版社: O'Reilly Media; 1 edition (25 Feb. 2012)
Language 语言: English
ISBN-10 书号: 1449303714
ISBN-13 书号: 9781449303716
If you’re an experienced programmer interested in crunching data,this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies,instead of a traditional math-heavy presentation.
Each chapter focuses on a specific problem in machine learning,such as classification,prediction,optimization,and recommendation. Using the R programming language,you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background,including business,government,and academic research.
Develop a naïve Bayesian classifier to determine if an email is spam,based only on its text
Use linear regression to predict the number of page views for the top 1,000 websites
Learn optimization techniques by attempting to break a simple letter cipher
Compare and contrast U.S. Senators statistically,based on their voting records
Build a “whom to follow” recommendation system from Twitter data
如果您是有兴趣处理数据的有经验的程序员，本书将让您开始使用机器学习 - 一种算法工具包，使计算机能够自动进行有用的任务自动化。作者Drew Conway和John Myles White通过一系列实际的案例研究来帮助您了解机器学习和统计工具，而不是传统的数学重复的演示。
每章重点介绍机器学习中的具体问题，如分类，预测，优化和推荐。使用R编程语言，您将学习如何分析样本数据集和编写简单的机器学习算法。黑客机器学习是任何背景下的程序员，包括商业，政府和学术研究的理想选择。
开发一个天真的贝叶斯分类器，以确定电子邮件是否是垃圾邮件，仅基于其文本
使用线性回归来预测前1000个网站的页面浏览量
尝试打破简单的字母密码来学习优化技术
根据他们的投票记录，对美国参议员进行统计比较和对比
从Twitter数据构建“谁跟随”推荐系统