Natural Language Processing (NLP)

This Program is designed to provide a comprehensive understanding of Natural Language Processing (NLP) techniques and applications. Participants will explore the foundational principles of NLP, including text preprocessing, tokenization, and sentiment analysis. The course will delve into advanced topics such as topic modeling, sequence models, and state-of-the-art transformer models like BERT. By the end of the course, participants will be proficient in using key NLP libraries and frameworks, preparing them for advanced studies or careers in NLP and AI.

• Understand and apply foundational NLP concepts and techniques.
• Perform text preprocessing, cleaning, and normalization.
• Implement tokenization, stemming, and lemmatization methods.
• Build and evaluate sentiment analysis models.
• Explore and apply topic modeling techniques such as LDA.
• Develop sequence models using RNNs and LSTM networks.
• Utilize transformer models like BERT for advanced NLP tasks.
• Gain hands-on experience with NLP libraries including NLTK, spaCy, and Hugging Face Transformers.
• Complete real-world NLP projects to demonstrate practical skills.
• Prepare for advanced roles in NLP and AI through comprehensive training and hands-on practice.

Introduction to Natural Language Processing:

• Overview of NLP and its Applications.
• Key Concepts and Terminologies in NLP.
• Text Preprocessing and Cleaning.

Text Processing and Tokenization:

• Techniques for Text Normalization.
• Tokenization Methods.
• Stemming and Lemmatization.

Sentiment Analysis:

• Basics of Sentiment Analysis.
• Building Sentiment Analysis Models.
• Evaluating Sentiment Analysis Models.

Topic Modeling:

• Introduction to Topic Modeling.
• Latent Dirichlet Allocation (LDA).
• Implementation of Topic Modeling in Python.

Sequence Models:

• Understanding Sequence Data.
• Recurrent Neural Networks (RNN).
• Long Short-Term Memory (LSTM) Networks.

Transformer Models:

• Introduction to Transformer Models.
• Bidirectional Encoder Representations from Transformers (BERT).
• Implementing Transformers for NLP Tasks.

Practical NLP:

• Working with Python and Jupyter Notebooks.
• Using NLTK and spaCy for NLP Tasks.
• Implementing Advanced NLP Models with Hugging Face Transformers.