Natural Language Processing

Make AI Programs Understand and Generate Human Language

4.4(56)3h 0m20 lectures2 sections

What you'll learn

  • Understand the lifecycle of NLP projects
  • Learn to prepare data and segment texts
  • Master word stemming techniques
  • Analyze and understand text sentiments
  • Select appropriate models and evaluate them
  • Design effective AI language programs

About this course

Natural Language Processing (NLP) is one of the most important branches of artificial intelligence aimed at enabling machines to understand and interact with human language naturally. NLP combines computer science, statistics, and computational linguistics to analyze texts and understand context and meanings. It serves as the foundation for many modern AI applications like smart personal assistants, chatbots, machine translation tools, and sentiment analysis on social media. Techniques like text segmentation, stopword removal, and vectorization transform textual data into a format machines can process. This enhances the performance of smart applications, making them more accurate and effective. In this course, you will learn how to prepare textual data, use NLP techniques, and apply them to build effective models, opening the door for you to develop AI applications with advanced linguistic capabilities.

Expected outcomes

  • Ability to implement a complete text processing project.
  • Analyze texts and extract features using advanced techniques.
  • Select and evaluate suitable models for optimal performance.
  • Improve textual data to meet the needs of different models.
  • Work efficiently on NLP projects using modern tools and techniques.

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    Course content

    1

    Natural Language Processing NLP

    19 lectures
    1. Introduction to NLP15:08
    2. Project Life Cycle11:57
    3. Data Preparation9:51
    4. Remove Punctuation6:36
    5. Tokenization & StopWords (Part 1)7:17
    6. Tokenization & StopWords (Part 2)8:42
    7. Stemming & Lemtizing (Part 1)7:42
    8. Stemming & Lemtizing (Part 2)7:12
    9. Vectorization (Part 1)14:11
    10. Vectorization (Part 2)5:42
    11. Vectorization (Part 3)7:27
    12. Feature Engineering (Part 1)12:02
    13. Feature Engineering (Part 2)8:47
    14. Model Selection (Part 1)14:20
    15. Model Selection (Part 2)6:51
    16. Model Evaluation (Part 1)11:53
    17. Model Evaluation (Part 2)3:16
    18. Model Evaluation (Part 3)14:23
    19. Model Evaluation (Part 4)7:35
    2

    Attachments

    1 attachments
    1. Matrials

    Instructor

    Eng. Ziad Mahmoud

    Eng. Ziad Mahmoud

    Teaching assistant at Faros University, Ziad has delivered over 3000 training and consulting hours to more than 600 trainees.
    1,402 students4 courses