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2025-06-03
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.
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.
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。
评分教你把Thinkers和Doers结合起来。思想与代码并举
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
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。 ...
评分如果你是机器学习的入门者,如果你想快速看到算法的执行效果,那么这本书适合你。 作者把算法的基本原理讲的很清楚,而且代码是完整可执行的。当然,如果你想了解算法背后的数学原理,还需要花时间去复习一下概率论、高等数学和线性代数。 BTW:读者最好有编程经验,有抽象思维。
评分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...
评分如果你是机器学习的入门者,如果你想快速看到算法的执行效果,那么这本书适合你。 作者把算法的基本原理讲的很清楚,而且代码是完整可执行的。当然,如果你想了解算法背后的数学原理,还需要花时间去复习一下概率论、高等数学和线性代数。 BTW:读者最好有编程经验,有抽象思维。
评分Machine Learning這門科學範圍很大,不大可能有一本書能在這個主題面面俱到。初學者需要先了解機器學習的範圍,再比較淺顯的去知道背後的理論基礎,之後再儘可能挖掘每一種算法的形成與直觀意義。在我閱讀過的機器學習書籍中,這本書與O'Reilly的Data Science From Scratch比較...
Machine Learning in Action pdf epub mobi txt 电子书 下载 2025