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Essential Natural Language Processing

  • Course Code: Artificial Intelligence - Essential Natural Language Processing
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for basic Python developers, analysts or others who want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the course for you.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Natural Language Processing skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for basic Python developers, analysts or others who want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the course for you. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Essential Natural Language Processing is a hands-on guide to NLP with practical techniques you can put into action right away. By following the numerous Python-based examples and real-world case studies, you’ll apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more. When you’re done, you’ll have a solid grounding in NLP that will serve as a foundation for further learning. 

Working in a hands-on learning environment, led by our Natural Language Processing expert instructor, students will learn about and explore: 

  • you’ll apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more.  
  • you’ll have a solid grounding in NLP that will serve as a foundation for further learning. 
  • provides a concrete example with practical techniques that you can put into practice right away. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Extracting information from raw text 
  • Named entity recognition 
  • Automating summarization of key facts 
  • Topic labeling 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling. 

Pre-Requisites:  Students should have  

  • For beginners to NLP with basic Python skills.  
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction 
  • A brief history of NLP 
  • Typical tasks 
  1. Your first NLP example 
  • Introducing NLP in practice: spam filtering 
  • Understanding the task 
  • Implementing your own spam filter 
  • Deploying your spam filter in practice 
  1. Introduction to Information Search 
  • Understanding the task 
  • Processing the data further 
  • Information weighing 
  • Practical use of the search algorithm 
  1. Information Extraction 
  • Use cases 
  • Understanding the task 
  • Detecting word types with part-of-speech tagging 
  • Understanding sentence structure with syntactic parsing 
  • Building your own Information Extraction algorithm 
  1. Author Profiling as a Machine Learning Task 
  • Understanding the task 
  • Machine Learning pipeline at a first glance 
  • A closer look at the machine learning pipeline 
  1. Linguistic Feature Engineering for Author Profiling 
  • Another close look at the machine learning pipeline 
  • Feature engineering for authorship attribution 
  • Practical use of authorship attribution and user profiling 
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