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
发表于2025-06-01
Machine Learning in Action pdf epub mobi txt 电子书 下载 2025
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.
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.
何必这么多具体的代码……
评分Bad Smells in Codes...
评分一般般
评分内容比较基础,有py代码,对着看比较容易理解。
评分内容比较基础,有py代码,对着看比较容易理解。
我的学习过程如下,供大家参考: 1、有些python的基础编程能力,如果没有,先花半个小时学习下; 2、数学基本统计基础,如果不懂数学原理,可以先不要去理解数学原理; 3、先上手写下代码,沉浸进入,熟悉了代码流程,再回头去看数据原理,就明白了。 5、一句话,先不求甚解,...
评分我的学习过程如下,供大家参考: 1、有些python的基础编程能力,如果没有,先花半个小时学习下; 2、数学基本统计基础,如果不懂数学原理,可以先不要去理解数学原理; 3、先上手写下代码,沉浸进入,熟悉了代码流程,再回头去看数据原理,就明白了。 5、一句话,先不求甚解,...
评分理论推导太弱,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...
Machine Learning in Action pdf epub mobi txt 电子书 下载 2025