Natural Language Processing Challenges : Exam Practice Tests

Course Overview

• Course Title: Natural Language Processing Challenges: Exam Practice Tests
• Instructor: Faisal Zamir
• Target Audience:
  • Students preparing for NLP exams
  • Aspiring NLP practitioners
  • Professionals seeking to enhance their NLP skills
• Prerequisites: Basic understanding of Python

Curriculum Highlights

• Key Topics Covered:
  • Basic Concepts in NLP
  • Tokenization and Text Preprocessing
  • NLP Libraries and Tools
  • Language Modeling
  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Part-of-Speech (POS) Tagging
  • Word Embeddings
  • Text Classification
  • Topic Modeling
  • Sequence-to-Sequence Models
  • Attention Mechanisms
  • Language Generation
  • Advanced NLP Architectures
  • Ethical Considerations in NLP
• Key Skills Learned:
  • Understanding and applying basic NLP concepts
  • Preprocessing text data
  • Using popular NLP libraries
  • Developing language models
  • Performing sentiment analysis
  • Recognizing and tagging named entities
  • Implementing word embeddings
  • Classifying text
  • Modeling topics from text corpora
  • Working with sequence-to-sequence models
  • Incorporating attention mechanisms
  • Generating text for various tasks
  • Exploring advanced NLP architectures
  • Addressing ethical challenges in NLP

Course Format

• Duration: 4 practice tests
• Format: Self-paced online course
• Resources: Access on mobile