Course Code: nlpbspk
Duration: 35 hours
Course Outline:

Day 1:

  • Introduction to Text Manipulation
    • Understanding strings in programming languages (Python focus).
    • Basic string operations: concatenation, slicing, and transformation.
  • Regular Expressions
    • Introduction to regular expressions for pattern searching in text.
    • Practical exercises: email extraction, phone number validation.
  • Python Libraries for Text Manipulation
    • Overview of str, re, string libraries.
    • Hands-on activities: cleaning and preparing text data.
  • Exercises
    • Data cleaning exercises.
    • Mini-project: Text preprocessing for a dataset.
  • Visualization
    • Introduction to text data visualization: frequency distributions.
    • Using libraries like Matplotlib and Seaborn for visualization.

Day 2:

  • Advanced Data Structures for Text Processing
    • Working with lists, dictionaries for text analysis.
    • Introduction to JSON and XML for structured text processing.
  • Parsing Complex Text Files
    • Extracting information from structured files (CSV, JSON, XML).
    • Extracting information from unstructured files (pdf, txt, .doc)
    • Hands-on: Parsing and transforming complex data into usable formats.
  • Exercises
    • JSON/XML data parsing and transformation exercises.
    • Visualization of complex data structures.

Day 3:

  • Foundations of Natural Language Processing (NLP)
    • Introduction to NLP and its applications.
    • Understanding syntax and semantics in NLP.
  • Machine Translation Techniques
    • Overview of translation techniques: statistical, rule-based, neural.
    • Comparative analysis of different translation models.
  • Exercises
    • Implementing a simple rule-based translation.
    • Analysis of translation model outputs for accuracy and fluency.
  • Visualization and Assessment
    • Visualizing translation model performance.
    • Error analysis and improving translation models.

Day 4:

  • Introduction to Named Entity Recognition (NER)
    • Understanding NER and its importance in text analysis.
    • Practical implementation of NER using libraries like spaCy or NLTK.
  • Advanced NLP Techniques
    • Overview of sentiment analysis, topic modeling.
    • Deep dive into machine learning models in NLP (e.g., LSTM, BERT).
  • Exercises
    • Hands-on NER tasks and sentiment analysis.
    • Building a simple topic model for a given dataset.
  • Visualization and Model Assessment
    • Visualizing NER results and sentiment trends.
    • Assessing model accuracy and handling biases.

Day 5:

  • Workshop: Introduction to Transfer Learning
    • Understanding the concept of transfer learning and its significance in NLP.
    • Hands-on session on how to use pre-trained models like BERT and GPT for text classification, sentiment analysis, and more.
  • Practical Exercise: Implementing Transfer Learning
    • Participants apply transfer learning to enhance their ongoing projects, leveraging pre-trained models to improve accuracy and efficiency.

Building GPT-driven Chatbots

  • Tutorial: Introduction to GPT and its Applications in Chatbots
    • Overview of Generative Pre-trained Transformer (GPT) models and their evolution.
    • Discussion on how GPT models can be used to create responsive and intelligent chatbots.
  • Live Coding Session: Developing a GPT-driven Chatbot
    • Step-by-step guidance on building a chatbot using GPT, focusing on dialogue management and user interaction.
    • Tips on fine-tuning GPT models for specific departmental needs and scenarios.