Python Regular Expressions - Real World Applied Python هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

*** NEW PREVIEW VIDEOS: All lectures in Section 2 and Section 3 enabled for preview!  Check it out! 

     NEW: Project 4 Network Configuration Parser added to the course***

Python Regular Expressions is a hands-on course that teaches you everything you need to know about Regular Expression using Python Language. Master cutting edge pattern matching, data extraction and data cleanup skills in this course.

Regular Expression is a powerful text processing tool for log mining, data parsing, cleanup and preparation.  Power and elegance of regular expression allows you to do complex data extraction and cleanup with very few lines of code.

Over 60% of the effort in big data projects is spent on data cleanup and preparation.Data can come from variety of sources including internal databases, log files, sensor generated data, Twitter, Facebook and so forth.  Having access to powerful regular expression tool will open up lot of opportunities for you on how you look at your data and what can you do with it.

This course contains over 25+ hands-on exercises, practical tips, quizzes and four projects to apply the new skills you learned in this class.  In the first project, we will be extracting useful information from unstructured text data from Robocopy tool, in the second project we will be on working on large data set generated by Sensors and in the third project we will look in to Health Care Systems that deal with Electronic Medical Records. ***NEW***Fourth project added on parsing Network Interface Configuration.

These exercises will demonstrate that with regular expression you can implement complex parsing with only a few lines of code. 

As a bonus, you will receive an Python Interactive Tool with Source Code for learning regular expression faster.

This course uses free Anaconda Python distribution tool for development and exercise.  This is an all video lecture with quizzes, full source code, downloadable list of data and patterns used.