Program Structure

Module 1: Introduction to Natural Language Processing

• Overview of Natural Language Processing and its importance in AI.
• Text preprocessing: tokenization, stopword removal, and stemming/lemmatization.
• Understanding text representations: Bag-of-Words, TF-IDF, and word embeddings (Word2Vec, GloVe).
• Hands-on exercise: Implementing basic NLP preprocessing techniques using Python and NLTK.

Module 2: Text Classification and Sentiment Analysis

• Introduction to text classification and sentiment analysis.
• Supervised learning algorithms for text classification: Logistic Regression, Naive Bayes, and SVM.
• Hands-on exercise: Building a sentiment analysis model using Scikit-learn and NLTK.

Module 3: Named Entity Recognition (NER) and Information Extraction

• Understanding Named Entity Recognition (NER) and its applications.
• Using spaCy for NER and extracting structured information from unstructured text.
• Hands-on exercise: Implementing NER for identifying people, organizations, and locations in text.

Module 4: Machine Translation and Text Generation

• Introduction to machine translation: Rule-based vs. statistical vs. neural machine translation (NMT).
• Exploring sequence-to-sequence models for text generation and translation tasks.
• Hands-on exercise: Building a simple machine translation model using Seq2Seq architecture in TensorFlow.

Module 5: Deep Learning for NLP: Transformers and BERT

• Introduction to deep learning for NLP: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM).
• Understanding transformers and attention mechanisms.
• Using pre-trained models like BERT for various NLP tasks.
• Hands-on exercise: Implementing a text classification model using a pre-trained BERT model.

Module 6: Question Answering Systems

• Building question answering systems using NLP techniques and deep learning models.
• Exploring extractive vs. generative QA models.
• Hands-on exercise: Building a simple QA system using Hugging Face Transformers library.

Module 7: NLP in Practice: Real-World Applications

• Exploring real-world applications of NLP: chatbots, voice assistants, recommendation systems.
• Case studies: How NLP is used in industries like healthcare, finance, and e-commerce.
• Hands-on exercise: Building a text summarization model or chatbot using NLP techniques.

Module 8: Model Evaluation and Optimization

• Evaluating NLP models: accuracy, precision, recall, F1-score, and perplexity.
• Hyperparameter tuning and optimization for NLP models.
• Hands-on exercise: Fine-tuning a pre-trained model for a specific NLP task.

Final Project