Machine Learning in Action

Peter Harrington holds Bachelors and Masters Degrees in Electrical Engineering. He worked for Intel Corporation for seven years in California and China. Peter holds five US patents and his work has been published in three academic journals. He is currently the chief scientist for Zillabyte Inc. Peter spends his free time competing in programming competitions, and building 3D printers.

出版者:Manning Publications

作者:Peter Harrington

页数:384

出版时间:2012-4-19

价格:GBP 29.99

装帧:Paperback

isbn号码:9781617290183

图书标签:机器学习MachineLearning数据挖掘python人工智能Python计算机科学算法

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It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades.

"Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers.

Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, 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.

As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.

读后感

机器学习是人工智能研究领域中一个极其重要的研究方向，在现今的大数据时代背景下，捕获数据并从中萃取有价值的信息或模式，成为各行业求生存、谋发展的决定性手段，这使得这一过去为分析师和数学家所专属的研究领域越来越为人们所瞩目。 本书第一部分主要介绍机器学习基础，以...  

Machine Learning這門科學範圍很大，不大可能有一本書能在這個主題面面俱到。初學者需要先了解機器學習的範圍，再比較淺顯的去知道背後的理論基礎，之後再儘可能挖掘每一種算法的形成與直觀意義。在我閱讀過的機器學習書籍中，這本書與O'Reilly的Data Science From Scratch比較...  

纯属好奇机器学习是怎么回事，虽然是coding渣，冲着现在三分热情在慕课上补了下python的基础知识。就跑来看实战。 下了kiddle版和pdf版本的看了第一章节，大学的矩阵相加，相减，相乘都忘光了， numpy的各个函数也不熟。看的很打击积极性。 遂又上51cto上 又搜机器学习的相关...  

特别适合新手，特别适合新手，特别适合新手。长度适中，举例形象，概念浅显通俗。难得有一个条理清楚 逻辑不迷糊 不堆砌代码打哈哈的书。基于这个理由bonus给五星，以后给别人推荐就这本了。 尤其是前面几章，介绍机器学习的基本概念。作者给我们指明了一个做ML的基本要求：“...  

为什么我会力荐这本书？ 也许书中分类器都非常的简单，数学理论都非常的粗浅（为了看明白书中SVM分类器的训练过程，不得不去复习了二次凸优化解法，自己推导被作者略去的中间过程），算法测试也只在轻量级的数据集上完成。 不过，大可不必像其他评论一样对贬低本书。聪明的读...  

用户评价

入门书籍。。超多python代码..

内容比较基础，有py代码，对着看比较容易理解。

随便翻翻，当复习Python和相关库了。适合初学者。

理论条理清楚、举重若轻。可惜程序代码水平稍差。

读了LR，ada boost，略读了svm，psvm。数学渣子的福音，码农最爱的实例。 虽然大家都说写的不好，不过入个门还是不错。