翻譯:Four steps to master machine learning with python (including free books & resources),來自LernPython. 這篇文章很爛,不過里面的資源匯總的不錯,這里相當(dāng)于限Mark下,后面準(zhǔn)備翻譯那些不錯的書和Paper,也歡迎更多的人加入.
想要理解和研究機(jī)器學(xué)習(xí),首先你應(yīng)該要掌握 Python 或者 R ,都是和 C, Java, PHP 差不多的語言(譯:差太多了好吧).不過呢, Python 和 R 都是比較年輕(譯:不懂, Python 可并不年輕吧),而且呢更高級,完全不用理解底層(譯:?),所以他倆都很容易學(xué). Python 更牛逼的地方在于她能夠處理更多的問題,比如,機(jī)器學(xué)習(xí),算法,圖像等,而不像 R 只能是進(jìn)行數(shù)據(jù)處理和分析. Python 有著更廣泛的應(yīng)用領(lǐng)域,比如 后端框架 Django (譯:原文是,'Hosting websites: Jango'),自然語言處理(譯: 原文是, 'natural language proecssing',作者太不認(rèn)真,NLP),網(wǎng)站接入等,而且 Python 更像 C 語言(譯:扯淡),所以她現(xiàn)在很流行.
毛子的原文里面有不少錯誤,我以自己的理解加以修正,僅供參考.語法文法錯誤我就直接修改,原文作者的表達(dá)內(nèi)容錯誤會依據(jù)原文不變,在
()內(nèi)說明.
新手用 Python 進(jìn)行機(jī)器學(xué)習(xí)的四個步驟
- Python 基礎(chǔ)知識學(xué)習(xí),有書,Mooc,視頻.
- 處理數(shù)據(jù),你得了解一些模塊,如:
Pandas,Numpy,Matplotlib和 Natural Language Processing. - 接著你就得爬取數(shù)據(jù),可以通過API,也可以直接到網(wǎng)站上去爬取.網(wǎng)站爬蟲模塊:
BeautifulSoup(譯:應(yīng)該是 Scrapy, BS 是 HTML/XML 解析器).我們用拿到的數(shù)據(jù)來訓(xùn)練算法. - 最后一步,就是要學(xué)習(xí) ML 的相關(guān)算法,以及工具
Scikit-learn.
1. 學(xué)習(xí) Python
學(xué)習(xí) Python 最簡單粗暴的法子就是到 Codecademy 上去注冊個賬號來學(xué)習(xí)基礎(chǔ)知識.一個被好多碼農(nóng)推薦的很經(jīng)典的網(wǎng)站 LearnPythonTheHardWay. Byte of Python 這篇文章是非常值得去學(xué)習(xí)的. Python社區(qū)還為新手給出了一個 Python 學(xué)習(xí)資源列表. O’Reilley 出版的一本書 Think Python, 這里可以免費(fèi)下載. 最后還有一個 Introduction to Python for Econometrics, Statistics and Data Analysis 也講了好多 Python 的基礎(chǔ)知識.
2. 導(dǎo)入模塊
做機(jī)器學(xué)習(xí)很重要的幾個模塊和工具是 NumPy, Pandas, Matplotlib 和 IPython.Data Analysis with Open Source Tools 這本書里面都有涉及這些內(nèi)容. 上面提到的 Introduction to Python for Econometrics, Statistics and Data Analysis 也涵蓋了這些東西.還有一本書 Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython.下面還有一些免費(fèi)的資源:
3. 爬取挖掘數(shù)據(jù)
一旦你掌握了 Python 的基礎(chǔ),下面就要學(xué)會怎么去爬取數(shù)據(jù). 也就是網(wǎng)頁爬蟲. 像 Twitter 和 LinkedIn 這些網(wǎng)站都給出了 API s接口,讓我們?nèi)カ@得文本數(shù)據(jù).關(guān)于這方面下面有幾本書不錯的書: Mining the Social Web(免費(fèi)), Web Scraping with Python 和 Web Scraping with Python: Collecting Data from the Modern Web.
最后這些文本數(shù)據(jù)要由 NLP 技術(shù)處理成數(shù)值化數(shù)據(jù):Natural language processing with Python . 圖像和視頻要用圖像處理 CV,下面有幾個不錯的資源: Programming Computer Vision with Python(免費(fèi)), Programming Computer Vision with Python: Tools and algorithms for analyzing images 和 Practical Python and OpenCV .
Python 爬蟲的一些例子:
- 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 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 Reviews
4. 機(jī)器學(xué)習(xí)
機(jī)器學(xué)習(xí)可以分為四部分: 分類, 聚類, 回歸和降維.
Scikit-learn 官網(wǎng)上有很多指南,下面列一些其它的:
- Introduction to Machine Learning 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
- Mapping 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 an 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
書:
- Collection of books on reddit
- Building Machine Learning Systems with Python
- Building Machine Learning Systems with Python, 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
機(jī)器學(xué)習(xí)相關(guān)的Blog和課程
在線課程: Collection of links . MOOC : machine learning 和 Data Analyst Nanodegree.
這里是一些Blog.
機(jī)器學(xué)習(xí)理論
書:
還有一些 Watch 15 hours theory of machine learning!
越看越懶得翻,著實(shí)沒什么營養(yǎng),索性直接列出資源.下面是美國麻省理工學(xué)院（MIT）博士林達(dá)華老師(ML大牛)推薦的書單.
Machine Learning
Pattern Recognition and Machine Learning
By Christopher M. Bishop
A 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, Exponential Families, and Variational Inference
By Martin J. Wainwright and Michael I. Jordan
It 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, and Think
Viktor Mayer-Schonberger, and Kenneth Cukier
A short but insightful manuscript that will motivate you to rethink how we should face the explosive growth of data in the new century.
Statistical Pattern Recognition (2nd/3rd Edition)
By Andrew R. Webb, and Keith D. Copsey
A well written book on pattern recognition for beginners. It covers basic topics in this field, including discriminant analysis, decision trees, feature selection, and clustering -- all are basic knowledge that researchers in machine learning or pattern recognition should understand.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
By Bernhard Schlkopf and Alexander J. Smola
A comprehensive and in-depth treatment of kernel methods and support vector machine. It not only clearly develops the mathematical foundation, namely the reproducing kernel Hilbert space, but also gives a lot of practical guidance (e.g. how to choose or design kernels.)
Mathematics
Topology (2nd Edition)
By James Munkres
A classic on topology for beginners. It provides a clear introduction of important concepts in general topology, such as continuity, connectedness, compactness, and metric spaces, which are the fundamentals that you have to grasped before embarking on more advanced subjects such as real analysis.
Introductory Functional Analysis with Applications
ByErwin Kreyszig
It is a very well written book on functional analysis that I would like to recommend to every one who would like to study this subject for the first time. Starting from simple notions such as metrics and norms, the book gradually unfolds the beauty of functional analysis, exposing important topics including Banach spaces, Hilbert spaces, and spectral theory with a reasonable depth and breadth. Most important concepts needed in machine learning are covered by this book. The exercises are of great help to reinforce your understanding.
Real Analysis and Probability (Cambridge Studies in Advanced Mathematics)
By R. M. Dudley
This is a dense text that combines Real analysis and modern probability theory in 500+ pages. What I like about this book is its treatment that emphasizes the interplay between real analysis and probability theory. Also the exposition of measure theory based on semi-rings gives a deep insight of the algebraic structure of measures.
Convex Optimization
By Stephen Boyd, and Lieven Vandenberghe
A classic on convex optimization. Everyone that I knew who had read this book liked it. The presentation style is very comfortable and inspiring, and it assumes only minimal prerequisite on linear algebra and calculus. Strongly recommended for any beginners on optimization. Note: the pdf of this book is freely available on the Prof. Boyd's website.
Nonlinear Programming (2nd Edition)
By Dimitri P. Bersekas
A thorough treatment of nonlinear optimization. It covers gradient-based techniques, Lagrange multiplier theory, and convex programming. Part of this book overlaps with Boyd's. Overall, it goes deeper and takes more efforts to read.
Introduction to Smooth Manifolds
By John M. Lee
This is the book that I used to learn differential geometry and Lie group theory. It provides a detailed introduction to basics of modern differential geometry -- manifolds, tangent spaces, and vector bundles. The connections between manifold theory and Lie group theory is also clearly explained. It also covers De Rham Cohomology and Lie algebra, where audience is invited to discover the beauty by linking geometry with algebra.
Modern Graph Theory
By Bela Bollobas
It is a modern treatment of this classical theory, which emphasizes the connections with other mathematical subjects -- for example, random walks and electrical networks. I found some messages conveyed by this book is enlightening for my research on machine learning methods.
Probability Theory: A Comprehensive Course (Universitext)
By Achim Klenke
This is a complete coverage of modern probability theory -- not only including traditional topics, such as measure theory, independence, and convergence theorems, but also introducing topics that are typically in textbooks on stochastic processes, such as Martingales, Markov chains, and Brownian motion, Poisson processes, and Stochastic differential equations. It is recommended as the main textbook on probability theory.
A First Course in Stochastic Processes (2nd Edition)
By Samuel Karlin, and Howard M. Taylor
A classic textbook on stochastic process which I think are particularly suitable for beginners without much background on measure theory. It provides a complete coverage of many important stochastic processes in an intuitive way. Its development of Markov processes and renewal processes is enlightening.
Poisson Processes (Oxford Studies in Probability)
By J. F. C. Kingman
If you are interested in Bayesian nonparametrics, this is the book that you should definitely check out. This manuscript provides an unparalleled introduction to random point processes, including Poisson and Cox processes, and their deep theoretical connections with complete randomness.
Programming
Structure and Interpretation of Computer Programs (2nd Edition)
By Harold Abelson, Gerald Jay Sussman, and Julie Sussman
Timeless classic that must be read by all computer science majors. While some topics and the use of Scheme as the teaching language seems odd at first glance, the presentation of fundamental concepts such as abstraction, recursion, and modularity is so beautiful and insightful that you would never experienced elsewhere.
Thinking in C++: Introduction to Standard C++ (2nd Edition)
By Bruce Eckel
While it is kind of old (written in 2000), I still recommend this book to all beginners to learn C++. The thoughts underlying object-oriented programming is very clearly explained. It also provides a comprehensive coverage of C++ in a well-tuned pace.
Effective C++: 55 Specific Ways to Improve Your Programs and Designs (3rd Edition)
By Scott Meyers
The Effective C++ series by Scott Meyers is a must for anyone who is serious about C++ programming. The items (rules) listed in this book conveys the author's deep understanding of both C++ itself and modern software engineering principles. This edition reflects latest updates in C++ development, including generic programming the use of TR1 library.
Advanced C++ Metaprogramming
ByDavide Di Gennaro
Like it or hate it, meta-programming has played an increasingly important role in modern C++ development. If you asked what is the key aspects that distinguishes C++ from all other languages, I would say it is the unparalleled generic programming capability based on C++ templates. This book summarizes the latest advancement of metaprogramming in the past decade. I believe it will take the place of Loki's "Modern C++ Design" to become the bible for C++ meta-programming.
Introduction to Algorithms (2nd/3rd Edition)
By Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein
If you know nothing about algorithms, you never understand computer science. This is book is definitely a classic on algorithms and data structures that everyone who is serious about computer science must read. This contents of this book ranges from elementary topics such as classic sorting algorithms and hash table to advanced topics such as maximum flow, linear programming, and computational geometry. It is a book for everyone. Everytime I read it, I learned something new.
Design Patterns: Elements of Reusable Object-Oriented Software
By Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides
Textbooks on C++, Java, or other languages typically use toy examples (animals, students, etc) to illustrate the concept of OOP. This way, however, does not reflect the full strength of object oriented programming. This book, which has been widely acknowledged as a classic in software engineering, shows you, via compelling examples distilled from real world projects, how specific OOP patterns can vastly improve your code's reusability and extensibility.
Structured Parallel Programming: Patterns for Efficient Computation
By Michael McCool, James Reinders, and Arch Robison
Recent trends of hardware advancement has switched from increasing CPU frequencies to increasing the number of cores. A significant implication of this change is that "free lunch has come to an end" -- you have to explicitly parallelize your codes in order to benefit from the latest progress on CPU/GPUs. This book summarizes common patterns used in parallel programming, such as mapping, reduction, and pipelining -- all are very useful in writing parallel codes.
Introduction to High Performance Computing for Scientists and Engineers
By Georg Hager and Gerhard Wellein
This book covers important topics that you should know in developing high performance computing programs. Particularly, it introduces SIMD, memory hierarchies, OpenMP, and MPI. With these knowledges in mind, you understand what are the factors that might influence the run-time performance of your codes.
CUDA Programming: A Developer's Guide to Parallel Computing with GPUs
By Shane Cook
This book provides an in-depth coverage of important aspects related to CUDA programming -- a programming technique that can unleash the unparalleled power of GPU computation. With CUDA and an affordable GPU card, you can run your data analysis program in the matter of minutes which may otherwise require multiple servers to run for hours.
