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Home » Technology » AI / Machine Learning » Essential Natural Language Processing

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    ESNALPL21E09

Who should attend & recommended skills:

Python experienced programmers looking to learn NLP

Who should attend & recommended skills

  • Python programmers seeking to learn natural language processing.
  • Skill-level: Foundation-level Natural Language Processing skills for Intermediate skilled team members. This is not a basic class.
  • Python programming: Basic (1-2 years’ experience) preferred.

About this course

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 will apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more. When you are done, you will have a solid grounding in NLP that will serve as a foundation for further learning.

Skills acquired & topics covered

  • Applying NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more
  • A solid grounding in NLP that will serve as a foundation for further learning
  • A concrete example with practical techniques that you can put into practice right away.
  • Extracting information from raw text
  • Named entity recognition
  • Automating summarization of key facts
  • Topic labeling

Course breakdown / modules

  • A brief history of NLP
  • Typical tasks

  • Introducing NLP in practice:spam filtering
  • Understanding the task
  • Implementing your own spam filter
  • Deploying your spam filter in practice

  • Understanding the task
  • Processing the data further
  • Information weighing
  • Practical use of the search algorithm

  • 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

  • Understanding the task
  • Machine Learning pipeline at a first glance
  • A closer look at the machine learning pipeline

  • 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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