Mastering Natural Language Processing (NLP) – Online Course

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.