Pattern

Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization. The module is free, well-document and bundled with 50+ examples and 350+ unit tests. Download Installation Pattern is written for Python 2.5+ (no support for Python 3 yet). To install Pattern so that the module is available in all Python scripts, from the command line do: > cd pattern-2.6 > python setup.py install If you have pip, you can automatically download and install from the PyPi repository: If none of the above works, you can make Python aware of the module in three ways: Quick overview pattern.web pattern.en The pattern.en module is a natural language processing (NLP) toolkit for English. pattern.search pattern.vector Case studies Related: logank1

Time Series analysis tsa — statsmodels 0.7.0 documentation statsmodels.tsa contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Estimation is either done by exact or conditional Maximum Likelihood or conditional least-squares, either using Kalman Filter or direct filters. Currently, functions and classes have to be imported from the corresponding module, but the main classes will be made available in the statsmodels.tsa namespace. Some related functions are also available in matplotlib, nitime, and scikits.talkbox. Descriptive Statistics and Tests Estimation

dfhoughton/StanfordCFG Beautiful Soup Documentation — Beautiful Soup v4.0.0 documentation Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. These instructions illustrate all major features of Beautiful Soup 4, with examples. This document covers Beautiful Soup version 4.12.1. You might be looking for the documentation for Beautiful Soup 3. This documentation has been translated into other languages by Beautiful Soup users: Getting help If you have questions about Beautiful Soup, or run into problems, send mail to the discussion group. When reporting an error in this documentation, please mention which translation you’re reading. Here’s an HTML document I’ll be using as an example throughout this document. Running the “three sisters” document through Beautiful Soup gives us a BeautifulSoup object, which represents the document as a nested data structure: $ apt-get install python3-bs4

ankmathur96/NLP-Learn Computer Networking : Principles, Protocols and Practice | INL: IP Networking Lab Computer Networking : Principles, Protocols and Practice (aka CNP3) is an ongoing effort to develop an open-source networking textbook that could be used for an in-depth undergraduate or graduate networking courses. The first edition of the textbook used the top-down approach initially proposed by Jim Kurose and Keith Ross for their Computer Networks textbook published by Addison Wesley. CNP3 is distributed under a creative commons license. The second edition takes a different approach. The new features of the second edition are : The second edition of the ebook is now divided in two main parts The first part of the ebook uses a bottom-up approach and focuses on the principles of the computer networks without entering into protocol and practical details. Numerous exercises are also provided as well as interactive quizzes that enable the students to verify their understanding of the different chapters and lab experiments with netkit and other software tools. First edition of the textbook

7 Major Players In Free Online Education By Jennifer Berry Imagine a world where free, college-level education was available to almost everyone. Believe it or not, you're living in that world right now. Online education has been around for decades, but in the past couple of years, interest has spiked for massive open online courses, otherwise known as MOOCs, according to Brian Whitmer, co-founder of Instructure, an education technology company that created the Canvas Network, a platform for open online courses. "Since 2012, MOOCs have caught the attention of the educational world due to their potential to disrupt how education is delivered and open up access to anyone with an Internet connection," Whitmer explains. Related: 7 Degrees You Can Earn While Keeping Your Job If this seems too good to be true, you should know that, like many endeavors, students will largely get out of these classes what they put into them. Read on to learn more about seven of the most popular MOOCs and some of the great free classes they offer. Coursera