Machine Learning in Action pdf epub mobi txt 电子书 下载 2025
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.
- 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.
具体描述
读后感
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，...
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。 ...
评分人工智能的脉络 机器学习是人工智能的一个分支。 人工智能的研究历史有着一条从以“推理”为重点，到以“知识”为重点，再到以“学习”为重点的自然、清晰的脉络。 机器学习是实现人工智能的一个途径，即以机器学习为手段解决人工智能中的问题。 从学习方式来讲，机器学习包括...
评分用户评价
没学习又想学机器学习的可以考虑从这本书入手。偏向于应用的一本不错的入门书
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。
评分Bad Smells in Codes...
评分入门好书
评分书中介绍了“十大机器学习算法”中的八种，虽然不深入但是讲解清楚容易理解和上手，是本佳作。从覆盖面上来看没涉及到随机森林算法和神经网络是一个小遗憾。
相关图书
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 qciss.net All Rights Reserved. 小哈图书下载中心 版权所有