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2024-11-01
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.
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。
評分Bad Smells in Codes...
評分基本沒有算法優化,所以還是給3星。
評分是本好書,有些章節還看的不是最明白。值得反復閱讀
評分隨便翻翻,當復習Python和相關庫瞭。適閤初學者。
客观说,完全不能当入门书。 缺少必要的证明过程,有些甚至连公式都没有。 我觉得既然要学习机器学习,光改改代码完全是不够的,起码还得知道各个算法的基本公式和过程,不幸的是,这本书没有。 就比如逻辑斯蒂回归那章,他连损失函数都没提,就开始说梯度法了。问题是梯度法的...
評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。
評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,...
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