Tutorials > Pattern Detection With Python Regular Expressions (Regex)

Pattern Detection With Python Regular Expressions (Regex)


Date: 27 August 2022 | Posted by: voska89
Pattern Detection With Python Regular Expressions (Regex)
Last updated 8/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 886.85 MB | Duration: 3h 20m


Hands-On : 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 - Use regex to locate and transform data of interest
Input Validation - Cross-Check Input and Improve Security by Preventing Injection Attacks
Learn Techniques to Write High-Performance Patterns
Complements Machine Learning Skills
Hands-on projects
Requirements
Familiarity with Python
Description
Hi, and welcome to the Pattern Detection with Python Regular Expressions (Regex) Course!This is a project driven course and in just a couple of hours, you will gain precise and relevant information that you can immediately apply to your 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
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Increase the speed of learning
Lecture 3 Source Code Download
Lecture 4 Anaconda Python Environment Housekeeping
Lecture 5 Weekly Study Group - Live Q&A
Section 2: Python Regex Features
Lecture 6 Downloadable Resources
Lecture 7 Introduction to Regex Features
Lecture 8 RE Module, Match method, Unit Testing
Lecture 9 Regex Best and Worst Performance
Lecture 10 RE Module - Search, FindAll, FindIter, Groups
Lecture 11 RE Module - Find and Replace, Split
Lecture 12 Interactive Tool
Section 3: Python Regex Language
Lecture 13 Downloadable Resources
Lecture 14 Single Character Patterns
Lecture 15 Anchors
Lecture 16 Character Classes
Lecture 17 Quantifiers
Lecture 18 HTML input validation example
Lecture 19 Input Validation Example (Browser)
Section 4: Python Regex Engine - Behind the scenes
Lecture 20 Downloadable Resources
Lecture 21 One character at a time
Lecture 22 Left to Right
Lecture 23 Unusual Behavior When Using FindAll
Lecture 24 Lab - Left to Right
Lecture 25 Greedy, Lazy and Backtracking Analogy
Lecture 26 Greedy, Lazy and Backtracking Examples
Lecture 27 Lab - Greedy, Lazy and Backtracking
Lecture 28 Groups, Backreference, Replacement
Lecture 29 Lab - Groups, Backreference, Replacement
Lecture 30 Look Ahead
Lecture 31 What is a mark character?
Lecture 32 Look Behind
Lecture 33 Look Behind – Why does the pattern not work?
Lecture 34 Exercise - Currency Symbol
Lecture 35 Solution - Currency Symbol
Lecture 36 Exercise - Match a number
Lecture 37 Solution - Match a number
Lecture 38 Exercise - List all cars not made by Honda
Lecture 39 Solution - List all cars not made by Honda
Lecture 40 Exercise - Webserver Log Parser
Lecture 41 Solution - Webserver Log Parser
Lecture 42 Exercise - Filter by price
Lecture 43 Solution - Filter by price
Lecture 44 Exercise - List cars that meet specified criteria
Lecture 45 Solution - List cars that meet specified criteria
Lecture 46 Exercise - Password Validation
Lecture 47 Solution - Password Validation
Section 5: Python Regex Performance
Lecture 48 Downloadable Resources
Lecture 49 Exponential degradation - example of bad patterns and performance implication
Lecture 50 How to correct performance issues and optimize pattern
Lecture 51 Compiled versus Module Methods
Section 6: Project 1 - Log Parser
Lecture 52 Log Data Parser Objective
Lecture 53 Exercise 1 - Write a pattern to capture header information
Lecture 54 Exercise 2 - Write a pattern to capture error message
Lecture 55 Exercise 3 - Write a pattern to capture metrics
Lecture 56 Solution - How to write log parser regex patterns
Lecture 57 Solution - Log Data to JSON
Section 7: Project 2 - IoT Sensor Data
Lecture 58 Sensor Data Parser Objective
Lecture 59 Exercise 1 - Capture Date Value
Lecture 60 Exercise 2 - Capture Temperature and Humidity Value
Lecture 61 Solution - How to write sensor data patterns
Lecture 62 Solution - Sensor Data to JSON
Section 8: Project 3 - Health Care Data
Lecture 63 Health care Data Parser Objective
Lecture 64 Exercise 1- Cleanup pattern
Lecture 65 Exercise 2 - Write a pattern to capture a row
Lecture 66 Exercise 3 - Write a pattern to capture a cell
Lecture 67 Solution - How to write health care data patterns
Lecture 68 Solution - Health care data to CSV
Section 9: Project 4 - Network Configuration Parser
Lecture 69 Network Configuration Parser
Lecture 70 Network Configuration Parser - Answer
Section 10: Interesting Question and Answers from the Discussion Forum
Lecture 71 How to Remove Embedded Comma Inside Double Quotes
Lecture 72 How to Extract Unit Number from Postal Address
Lecture 73 Unusual Behavior When Using FindAll
Lecture 74 How to split text that uses comma and/or newline as separators
Section 11: Conclusion
Lecture 75 Congratulations!
Data Scientists,System Administrators,Data Analysts,Developers


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https://www.udemy.com/course/python-regular-expressions/


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