Regular Expressions (Regex) With Python - Easy And Fast!

Learn with Examples - Detect Patterns in Data, Verify Input, Improve Security, Data Cleanup

What you'll learn
Pattern Detection - Look for occurrences of a pattern using a concise language
Data Preparation - Locate and transform data of interest
Data Validation - Validate Input and Improve Security by Preventing Injection Attacks
Learn Techniques to Write High-Performance Patterns
Hands-on projects
Complements Machine Learning Skills

Requirements
Familiarity with Python

Description
Hi, and welcome to the Regular Expressions (Regex) with Python - Easy and Fast!Regular Expression (regex) is a pattern detection language – they are typically used to search patterns in text, extract matching values, and data validation. Regex is supported in many programming languages, including Python, C#, JavaScript, Perl, SQL, and more.This course is designed to provide hands-on experience with regular expressions through various exercises and projectsI am Chandra Lingam, and I am your instructor.Here are some typical uses of regular expressionPattern DetectionLook for occurrences of a pattern using a concise languageData PreparationData clean-up and preparation is often one of the most time-consuming activitiesYou can define the structure of data as a regex pattern and parse dataOne good application of this is AWS Glue and Athena.You can use regex to define the structure of a record in a plain text file, Create a table and query the file using SQLInput ValidationYou can implement a client-side check for input validationFor example, your app can guide the user to provide data in the correct format using regex.As part of the zero-trust architecture, you need to validate input to your microserviceWith regex, you can verify and validate data payloads in your serviceCloud ServicesSeveral cloud services use regex for advanced configuration.With the AWS web application firewall, you can allow or deny traffic based on a regex patternIn Google Workspace, you can use regex for content filtering, Gmail route configuration, and to search for content in google docsIn Google Analytics, you can use regex to locate and transform matching data in your data setRegex is also supported by several products such as SAP, Oracle, and SQL ServerCurriculumThe source code for this course is distributed using Github – so, you always have access to up-to-date codeAs part of resources, you will get this high-quality cheat-sheet for regex languageAnd an interactive regex tool to write patternsIn the Python Regex features section, you will get familiar with various regex methods, their purpose, and how to unit test your patternIn the regex language section, you will learn how to write patterns – starting from the simplest of patternsYou will also learn to incorporate regex in your HTML input types for validationRegex engine puts the onus on the developers, that is us, to write efficient patternsIn this section, you will gain knowledge of regular expression engine that will help you write optimal patternsThere are several exercises for you to apply your new skillsWe then look at performance and how poorly written patterns can degrade exponentiallyYou will learn how to optimize the patterns and address performance issuesThere are four hands-on projects in this courseYou will learn how to apply the regex for distinctly different data sets – unstructured log data, IoT sensor data, and parsing medical test data in HTML formatYou will get prompt support through the course Q&A forum and private messaging.I am looking forward to meeting youThank You!Chandra LingamCloud Wave LLC

Who this course is for:
Data Scientists,System Administrators,Data Analysts,Developers