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

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

这本书的最大好处是让你能够用最基本的pyton语法，从底层上让你构建代码，实现我们常说的比如邮件过滤，数据分类的应用。很多时候你要写最基本的代码和结构去做这些工作，而不是像kaggle的tutorial或者其他的工程大多数告诉你一个lib库函数去调用，你能看到底层在干什么...  

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...  

理论没讲太明白，直接上算法，甚至还有公式缺失，代码不敢恭维 就像大家说的一样 先看看线性代数、概率论、统计学再来看看这书吧 我这10多年 php、java、c#、js通吃，本想python应该不难，竟然代码部分有东西看不懂了，不得不拿起本python的书对着看...  

尽管评论里对这本书褒贬不一，我觉得这些都是根据每个人不同的能力背景出发而给的评论。而对于我这样能力的人来说，这本书可以说是最适合了。我是什么能力状况呢，计算机专业背景，有那么几年开发经验，但是机器学习方面是小白。 看这本书需要一定的编程经验，但不需要很强，...  

机器学习是概率统计的高级应用，数学知识很重要，要先掌握的先修课程有，微积分，线性代数，概率统计，多元微积分，微分方程，离散数学，数值分析，最优化，数学建模，掌握机器学习和深度学习算法，还有熟悉一种编程语言，有了这些基础，才能得心应手，机器学习主要应用在数据...  

用户评价

教你把Thinkers和Doers结合起来。思想与代码并举

入门书籍。。超多python代码..

是本好书，有些章节还看的不是最明白。值得反复阅读

随便翻翻，当复习Python和相关库了。适合初学者。

一般般