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.
发表于2025-04-01
Machine Learning in Action 2025 pdf epub mobi 电子书
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,...
评分 评分客观说,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...
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...
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
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.
没学习又想学机器学习的可以考虑从这本书入手。偏向于应用的一本不错的入门书
评分读了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。
评分基本没有算法优化,所以还是给3星。
评分哇FP growth简直美
Machine Learning in Action 2025 pdf epub mobi 电子书