Machine Learning in Action pdf epub mobi txt 电子书下载 2025

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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

出品人:

页数:384

译者:

出版时间:2012-4-19

价格:GBP 29.99

装帧:Paperback

isbn号码:9781617290183

丛书系列:

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.

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读后感

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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，...

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如果你是机器学习的入门者，如果你想快速看到算法的执行效果，那么这本书适合你。作者把算法的基本原理讲的很清楚，而且代码是完整可执行的。当然，如果你想了解算法背后的数学原理，还需要花时间去复习一下概率论、高等数学和线性代数。 BTW：读者最好有编程经验，有抽象思维。

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1. 这本书的价值是提供了一系列有趣的「实验作业」和「对应的数据」，以及乱七八糟的 Python 代码，迫使读者在同样数据集上自己写一个更好的。 2. 作者的 Python 代码写得真的真的很渣。 3. 作者的 SVM 写错了，不是 Platt 的原始 SMO 算法，里面的 error cache 形同虚设。 ...

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原书的案例、数据和代码（我自己基于Python3写的）都放在这里啦：https://github.com/Y1ran/Machine-Learning-in-Action-Python3 ，大家可以参考一下，记得star哦 PS. 忍不住吐槽：原书本来的代码除了简单易懂，实在找不出其他优点了。。 PSS.目前还在读，这个月会慢慢写完的，...