书籍介绍
这本书最鲜明的定位是“实战入门”：它把分类、回归、聚类、关联分析等核心算法逐一铺开，每类都给出清晰的优缺点与适用数据类型，帮助读者快速建立对各类方法的判断力。读者反馈中最常被提起的，是它用可运行的Python代码把抽象概念讲透——许多原本模糊的知识点，正是对着例子逐行跑通后才真正明白。但与此同时，争议也集中于此：有人嫌算法数学被过度简化，需另翻教材补足原理；也有人抱怨代码篇幅过密、风格欠佳，甚至因Python版本差异遇到运行问题。正因如此，它更适合以“动手熟悉”为首要目标的初学者，而非追求理论严谨的研究者。读懂它，你获得的是进入机器学习的入场券，而非精通它的通行证。
作者简介
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.
AI导读
核心看点
- 以Python代码实现经典机器学习算法
- 涵盖分类、回归及无监督学习核心内容
- 强调从底层构建代码理解算法原理
读者共识
- 优秀的机器学习入门实战指南
- 代码实现有助于直观理解概念
- 数学理论较浅需配合其他教材
精彩摘录
- "Pros: High accuracy, insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values The first machine-learning algorithm we’ll look at is k-Nearest Neighbors (kNN). It works like this: we have an existing set of"
- "Pros: Computationally cheap to use, easy for humans to understand learned results, missing values OK, can deal with irrelevant features Cons: Prone to overfitting Works with: Numeric values, nominal values"
- "General approach to decision trees 1. Collect: Any method. 2. Prepare: This tree-building algorithm works only on nominal values, so any continuous values will need to be quantized. 3. Analyze: Any method. You should visually inspect the tree after it is built. 4. Train: Construct a tree data struct"
- "Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values"
- "The clear syntax of Python has earned it the name executable pseudo-code."
- "With Python, you can program in any style you’re familiar with: object-oriented, procedural, functional, and so on."
- "With Python it’s easy to process and manipulate text"
- "Python is popular in the scientific and financial communities as well.A number of scientific libraries such as SciPy and NumPy allow you to do vector and matrix operations."
目录
Part 1: Classification
1 Machine learning basics
2 Classifying with k-nearest neighbors
3 Splitting datasets one feature at a time: decision trees
4 Classifying with probability distributions: Na�ve Bayes
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用户评论
python和机器学习浅显地入门材料。很有趣。
超级赞的入门好书，很多之前模糊的概念都通过本书中的例子弄明白了
一般般
随便翻翻，当复习Python和相关库了。适合初学者。
入门书籍。。超多python代码..
业界的书接地气
第三章决策树，说好的要用决策树测试一个实例，结果根本就没有测试的内容，让人失望。
一般吧，简单入门书
这本书适合理论功底强，而实践能力弱的人读。算法的理论部分极其鸡肋，有些算法根本讲不清楚，需要自己查阅资料去学习。作为一本重实践的书，可以没有公式推导，但书中出现的公式要求描述清楚不过分吧?另外最近几年机器学习飞速发展，本书显然略显老旧，书中代码是Python2的，很多需要安装的module并没有Python3的版本。还有一些抓取网站数据的，网站已经不存在了。一些近年来火爆的算法并没有包含，比如深度学习。但是作为新人的入门书籍，本书还是很好读的，可以了解一些机器学习的基本问题，基本算法以及基本面貌。最主要的是，学习一些作者好的代码习惯。
算法
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