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1、想要理解和研究机器学习,首先你应该要掌握 Python 或者 R ,都是和 C, Java, PHP 差不多的语言(译:差太多了好吧).不过呢, Python 和 R 都是比较年轻(译:不懂, Python 可并不年轻吧),而且呢更高级,完全不用理解底层(译:?),所以他俩都很容易学. Python 更牛逼的地方在于她能够处理更多的问题,比如,机器学习,算法,图像等,而不像 R 只能是进行数据处理和分析. Python 有着更广泛的应用领域,比如 后端框架 Django (译:原文是,Hosting websites: Jango),自然语言处理(译: 原文是, natural languag
2、e proecssing,作者太不认真,NLP),网站接入等,而且 Python 更像 C 语言(译:扯淡),所以她现在很流行.毛子的原文里面有不少错误,我以自己的理解加以修正,仅供参考.语法文法错误我就直接修改,原文作者的表达内容错误会依据原文不变,在()内说明.新手用 Python 进行机器学习的四个步骤 Python 基础知识学习,有书,Mooc,视频. 处理数据,你得了解一些模块,如: Pandas, Numpy, Matplotlib 和 Natural Language Processing. 接着你就得爬取数据,可以通过API,也可以直接到网站上去爬取.网站爬虫模块: Beaut
3、ifulSoup(译:应该是 Scrapy, BS 是 HTML/XML 解析器).我们用拿到的数据来训练算法. 最后一步,就是要学习 ML 的相关算法,以及工具 Scikit-learn.1. 学习 Python学习 Python 最简单粗暴的法子就是到 Codecademy 上去注册个账号来学习基础知识.一个被好多码农推荐的很经典的网站 LearnPythonTheHardWay. Byte of Python 这篇文章是非常值得去学习的. Python社区还为新手给出了一个 Python 学习资源列表. OReilley 出版的一本书 Think Python, 这里可以免费下载. 最后
4、还有一个 Introduction to Python for Econometrics, Statistics and Data Analysis 也讲了好多 Python 的基础知识.2. 导入模块做机器学习很重要的几个模块和工具是 NumPy, Pandas, Matplotlib 和 IPython.Data Analysis with Open Source Tools 这本书里面都有涉及这些内容. 上面提到的 Introduction to Python for Econometrics, Statistics and Data Analysis 也涵盖了这些东西.还有一本书 Py
5、thon for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython.下面还有一些免费的资源: 10 minutes to Pandas Pandas for machine learning 100 NumPy exercises3. 爬取挖掘数据一旦你掌握了 Python 的基础,下面就要学会怎么去爬取数据. 也就是网页爬虫. 像 Twitter 和 LinkedIn 这些网站都给出了 API s接口,让我们去获得文本数据.关于这方面下面有几本书不错的书: Mining the Social Web(免费), Web
6、Scraping with Python 和 Web Scraping with Python: Collecting Data from the Modern Web. 最后这些文本数据要由 NLP 技术处理成数值化数据:Natural language processing with Python . 图像和视频要用图像处理 CV,下面有几个不错的资源: Programming Computer Vision with Python(免费), Programming Computer Vision with Python: Tools and algorithms for analyzin
7、g images 和 Practical Python and OpenCV .Python 爬虫的一些例子: Mini-Tutorial: Saving Tweets to a Database with Python Web Scraping Indeed for Key Data Science Job Skills Case Study: Sentiment Analysis On Movie Reviews First Web Scraper Sentiment Analysis of Emails Simple Text Classification Basic Sentiment
8、 Analysis with Python Twitter sentiment analysis using Python and NLTK Second Try: Sentiment Analysis in Python Natural Language Processing in a Kaggle Competition for Movie Reviews4. 机器学习机器学习可以分为四部分: 分类, 聚类, 回归和降维.Machine learning in PythonScikit-learn 官网上有很多指南,下面列一些其它的: Introduction to Machine Lea
9、rning with Python and Scikit-Learn Data Science in Python Machine Learning for Predicting Bad Loans A Generic Architecture for Text Classification with Machine Learning Using Python and AI to predict types of wine Advice for applying Machine Learning Predicting customer churn with scikit-learn Mappi
10、ng Your Music Collection Data Science in Python Case Study: Sentiment Analysis on Movie Reviews Document Clustering with Python Five most popular similarity measures implementation in python Case Study: Sentiment Analysis on Movie Reviews Will it Python? Text Processing in Machine Learning Hacking a
11、n epic NHL goal celebration with a hue light show and real-time machine learning Vancouver Room Prices Exploring and Predicting University Faculty Salaries Predicting Airline Delays书: Collection of books on reddit Building Machine Learning Systems with Python Building Machine Learning Systems with P
12、ython, 2nd Edition Learning scikit-learn: Machine Learning in Python Machine Learning Algorithmic Perspective Data Science from Scratch First Principles with Python Machine Learning in Python机器学习相关的Blog和课程在线课程: Collection of links . MOOC : machine learning 和 Data Analyst Nanodegree.这里是一些Blog.机器学习理论
13、The Elements of statistical Learning Introduction to Statistical Learning书: Introduction to machine learning A Course in Machine Learning. 还有一些 Watch 15 hours theory of machine learning!越看越懒得翻,着实没什么营养,索性直接列出资源.下面是美国麻省理工学院（MIT）博士林达华老师(ML大牛)推荐的书单.Machine LearningPattern Recognition and Machine Learnin
14、gBy Christopher M. BishopA new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Bayesian perspective. It is a must read for people who intends to perform research on Bayesian learning and probabilistic inference.Graphical Models, Expon
15、ential Families, and Variational InferenceBy Martin J. Wainwright and Michael I. JordanIt is a comprehensive and brilliant presentation of three closely related subjects: graphical models, exponential families, and variational inference. This is the best manuscript that I have ever read on this subject. Strongly recommended to everyone interested in graphical models. The connections between various inference algorithms and convex optimization is clearly explained. Note: pdf version of this book is freely available online.Big Data: A Revolution That Will Transform How We Live, Work,
