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.
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.
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...
评分特别适合新手,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:“...
评分特别适合新手,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:“...
评分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。 ...
评分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...
教你把Thinkers和Doers结合起来。思想与代码并举
评分Bad Smells in Codes...
评分教你把Thinkers和Doers结合起来。思想与代码并举
评分是本好书,有些章节还看的不是最明白。值得反复阅读
评分读了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。
本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度,google,bing,sogou 等
© 2025 book.wenda123.org All Rights Reserved. 图书目录大全 版权所有