Program Objectives:

• 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.

What you will learn?

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.