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2024-06-18
Machine Learning in Action 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
随便翻翻,当复习Python和相关库了。适合初学者。
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.
评分是本好书,有些章节还看的不是最明白。值得反复阅读
评分何必这么多具体的代码……
评分教你把Thinkers和Doers结合起来。思想与代码并举
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.
特别适合新手,特别适合新手,特别适合新手。长度适中,举例形象,概念浅显通俗。难得有一个条理清楚 逻辑不迷糊 不堆砌代码打哈哈的书。基于这个理由bonus给五星,以后给别人推荐就这本了。 尤其是前面几章,介绍机器学习的基本概念。作者给我们指明了一个做ML的基本要求:“...
评分 评分理论没讲太明白,直接上算法,甚至还有公式缺失,代码不敢恭维 就像大家说的一样 先看看线性代数、概率论、统计学再来看看这书吧 我这10多年 php、java、c#、js通吃,本想python应该不难,竟然代码部分有东西看不懂了,不得不拿起本python的书对着看...
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...
评分理论没讲太明白,直接上算法,甚至还有公式缺失,代码不敢恭维 就像大家说的一样 先看看线性代数、概率论、统计学再来看看这书吧 我这10多年 php、java、c#、js通吃,本想python应该不难,竟然代码部分有东西看不懂了,不得不拿起本python的书对着看...
Machine Learning in Action 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024