圖書標籤: 機器學習 MachineLearning 數據挖掘 python 人工智能 Python 計算機科學 算法
发表于2024-11-25
Machine Learning in Action pdf epub mobi txt 電子書 下載 2024
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.
看這書可以同時入門機器學習,python,mapreduce,作者可以幾個方麵都講清楚,真不容易
評分內容比較基礎,有py代碼,對著看比較容易理解。
評分超級贊的入門好書,很多之前模糊的概念都通過本書中的例子弄明白瞭
評分內容比較基礎,有py代碼,對著看比較容易理解。
評分讀瞭LR,ada boost,略讀瞭svm,psvm。數學渣子的福音,碼農最愛的實例。 雖然大傢都說寫的不好,不過入個門還是不錯。
Python数据分析与机器学习实战 课程观看地址:http://www.xuetuwuyou.com/course/167 课程出自学途无忧网:http://www.xuetuwuyou.com 课程风格通俗易懂,真实案例实战。精心挑选真实的数据集为案例,通过python数据科学库numpy,pandas,matplot结合机器学习库scikit-lear...
評分人工智能的脉络 机器学习是人工智能的一个分支。 人工智能的研究历史有着一条从以“推理”为重点,到以“知识”为重点,再到以“学习”为重点的自然、清晰的脉络。 机器学习是实现人工智能的一个途径,即以机器学习为手段解决人工智能中的问题。 从学习方式来讲,机器学习包括...
評分理论推导太弱,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...
評分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...
評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...
Machine Learning in Action pdf epub mobi txt 電子書 下載 2024