Course Overview

The Natural Language Processing (NLP) program is a comprehensive 12-week course designed to equip participants with the knowledge and skills to master modern NLP techniques. From text preprocessing and tokenization to advanced transformer models like BERT, this course covers the foundational principles of NLP as well as cutting-edge tools and methods. By the end of the course, participants will be proficient in using popular NLP libraries such as NLTK, spaCy, and Hugging Face Transformers, preparing them for advanced studies or careers in NLP and AI.

Course Goals

This course aims to provide participants with a solid understanding of NLP concepts and the ability to apply them to real-world problems. By mastering both foundational and advanced NLP techniques, participants will be prepared to take on new challenges in AI and natural language understanding.

Program Objectives

• Foundational NLP Mastery: Gain a deep understanding of key NLP concepts, including text preprocessing, tokenization, and sentiment analysis.
• Hands-On Experience: Develop practical skills through hands-on projects, learning to apply topic modeling, sequence models, and transformer models like BERT.
• Tool Proficiency: Master the use of key NLP libraries like NLTK, spaCy, and Hugging Face Transformers for real-world NLP tasks.
• Career-Ready Skills: Prepare for advanced roles in NLP and AI through comprehensive training and practical applications.

Program Structure

• Module 1: Introduction to Natural Language Processing (NLP)
  • Overview of NLP and its applications
  • Key concepts and terminology
  • Introduction to text preprocessing and cleaning
• Module 2: Text Processing and Tokenization
  • Techniques for text normalization
  • Tokenization, stemming, and lemmatization
  • Practical implementations using Python
• Module 3: Sentiment Analysis
  • Basics of sentiment analysis
  • Building and evaluating sentiment analysis models
  • Tools and techniques for real-world applications
• Module 4: Topic Modeling
  • Introduction to topic modeling
  • Latent Dirichlet Allocation (LDA) explained
  • Hands-on implementation of topic modeling in Python
• Module 5: Sequence Models
  • Understanding sequence data and its challenges
  • Building Recurrent Neural Networks (RNNs)
  • Implementing Long Short-Term Memory (LSTM) networks for sequential data
• Module 6: Transformer Models
  • Introduction to transformer models
  • Understanding BERT (Bidirectional Encoder Representations from Transformers)
  • Using transformers for advanced NLP tasks
• Module 7: Practical NLP
  • Working with Python and Jupyter Notebooks
  • Implementing NLP tasks with NLTK and spaCy
  • Advanced NLP models with Hugging Face Transformers

Eligibility

• Senior undergraduates and graduate students in computer science or related fields
• Professionals in IT, data science, and software development looking to enhance their NLP skills

Learning Outcomes

• Master the foundational concepts and techniques in NLP
• Gain proficiency in text preprocessing, tokenization, and sentiment analysis
• Build advanced models using topic modeling, sequence models, and transformers
• Develop practical skills in popular NLP libraries like NLTK, spaCy, and Hugging Face Transformers
• Apply these skills to real-world NLP projects
• Earn a recognized certificate of completion, enhancing your professional credentials