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
理论推导太弱，导致部分代码实现难以理解为什么是这样写，建议配合吴恩达讲义使用。 另外贝叶斯那段代码实现应该是错误的，作者在计算概率的时候把分母给弄错了，还有就是因为python版本问题，在python3上跑书上程序需要对程序进行一些改动。 附代码修改： def classifyNB(vec2...
评分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...
评分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...
评分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...
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，...
用户评价
教你把Thinkers和Doers结合起来。思想与代码并举
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。
评分超级赞的入门好书，很多之前模糊的概念都通过本书中的例子弄明白了
评分over simplified in maths, you do need refer to other textbooks for get better idea how it works. and too much coding details, I can understand as the author was from CS background, but I think you need read more, beside this is indeed a nice start point.
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