.NET Framework Regular Expressions Regular expressions provide a powerful, flexible, and efficient method for processing text. The extensive pattern-matching notation of regular expressions enables you to quickly parse large amounts of text to find specific character patterns; to validate text to ensure that it matches a predefined pattern (such as an e-mail address); to extract, edit, replace, or delete text substrings; and to add the extracted strings to a collection in order to generate a report. For many applications that deal with strings or that parse large blocks of text, regular expressions are an indispensable tool. The centerpiece of text processing with regular expressions is the regular expression engine, which is represented by the System.Text.RegularExpressions.Regex object in the .NET Framework. At a minimum, processing text using regular expressions requires that the regular expression engine be provided with the following two items of information: The regular expression pattern (Mr\.?
Microsoft ODBC Driver for SQL Server on Linux The ODBC driver for SQL Server allows native applications (C/C++) running on Linux to connect to SQL Server 2008, SQL Server 2008 R2, SQL Server 2012, and Microsoft Azure SQL Database. With Microsoft ODBC Driver 13 (Preview) for SQL Server, SQL Server 2014 and SQL Server 2016 (Preview), are now also supported. Always Encrypted The following are answers to questions about the ODBC Driver for SQL Server on Linux. Our Lessons Our lessons are developed collaboratively on GitHub. You can check the status of each lesson on our dashboard, or view the nightly build. You may also enjoy Data Carpentry's lessons, which focus on data organization, cleanup, analysis, and visualization. All of our lessons are freely available under the Creative Commons - Attribution License.
5 open source natural language processing tools Text: it's everywhere. It fills up our social feeds, clutters our inboxes, and commands our attention like nothing else. It is oh so familiar, and yet, as a programmer, it is oh so strange. We learn the basics of spoken and written language at a very young age and the more formal side of it in high school and college, yet most of us never get beyond very simple processing rules when it comes to how we handle text in our applications. And yet, by most accounts, unstructured content, which is almost always text or at least has a text component, makes up a vast majority of the data we encounter. Don't you think it is time you upgraded your skills to better handle text?
8 Regular Expressions You Should Know Regular expressions are a language of their own. When you learn a new programming language, they're this little sub-language that makes no sense at first glance. Many times you have to read another tutorial, article, or book just to understand the "simple" pattern described. Today, we'll review eight regular expressions that you should know for your next coding project. Before we start, you might want to check out some of the regex apps on Envato Market, such as: A Beginner's Guide to Scaling to 11 Million+ Users on Amazon's AWS How do you scale a system from one user to more than 11 million users? Joel Williams, Amazon Web Services Solutions Architect, gives an excellent talk on just that subject: AWS re:Invent 2015 Scaling Up to Your First 10 Million Users. If you are an advanced AWS user this talk is not for you, but it’s a great way to get started if you are new to AWS, new to the cloud, or if you haven’t kept up with with constant stream of new features Amazon keeps pumping out. As you might expect since this is a talk by Amazon that Amazon services are always front and center as the solution to any problem.
RASA NLU gives developers an open source solution for natural language processing For better or worse, 2016 was another year of bots. I probably got more pitches for bot startups than anything else. And yet, bots are far from hitting their stride. If we hope to break beyond the rigid functionality of today’s tools, a prerequisite is going to be giving bot developers a bit more open source love.
Regular Expression Tutorial - Learn How to Use Regular Expressions This tutorial teaches you all you need to know to be able to craft powerful time-saving regular expressions. It starts with the most basic concepts, so that you can follow this tutorial even if you know nothing at all about regular expressions yet. The tutorial doesn't stop there. It also explains how a regular expression engine works on the inside, and alert you at the consequences. This helps you to quickly understand why a particular regex does not do what you initially expected. It will save you lots of guesswork and head scratching when you need to write more complex regexes.
Choosing R or Python for data analysis? An infographic I think you'll agree with me if I say: It's HARD to know whether to use Python or R for data analysis. And this is especially true if you're a newbie data analyst looking for the right language to start with. It turns out that there are many good resources that can help you to figure out the strengths and weaknesses of both languages. Introduction to Natural Language Processing (NLP) 2016 - Algorithmia Natural Language Processing Summary The field of study that focuses on the interactions between human language and computers is called Natural Language Processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia). “Natural Language Processing is a field that covers computer understanding and manipulation of human language, and it’s ripe with possibilities for newsgathering,” Anthony Pesce said in Natural Language Processing in the kitchen.
Learn REGEX - RegExOne LINKS You're done! For now that is... I'm always looking to add more examples and lessons, so please shoot me an email (at regexone@gmail.com) if you have any suggestions or questions! Below are a number of other resources about regular expressions on the web, the most useful of which may be the documentation for whichever language that you use regular expressions in. Hope you enjoyed the lessons and examples! R vs Python: head to head data analysis There have been dozens of articles written comparing Python and R from a subjective standpoint. We’ll add our own views at some point, but this article aims to look at the languages more objectively. We’ll analyze a dataset side by side in Python and R, and show what code is needed in both languages to achieve the same result. This will let us understand the strengths and weaknesses of each language without the conjecture. At Dataquest, we teach both languages, and think both have a place in a data science toolkit. We’ll be analyzing a dataset of NBA players and their performance in the 2013-2014 season.