Machine Learning in Action 在线电子书 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2024-06-09
Machine Learning in Action 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
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。
评分理论条理清楚、举重若轻。可惜程序代码水平稍差。
评分看这书可以同时入门机器学习,python,mapreduce,作者可以几个方面都讲清楚,真不容易
评分理论条理清楚、举重若轻。可惜程序代码水平稍差。
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。
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.
这本书最大的优点在于有源码实现,很赞,但是理论部分太差了,看了逻辑回归和支持向量机两章,发现好多理论都没讲,就比如逻辑回归中的Cost函数都没说,如果不了解,源码读起来也是一头雾水,所以对于初学者还需要一本理论较强的书,推荐李航博士的统计机器学习方法,刚好配套~
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...
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...
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,...
Machine Learning in Action 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024