Natural Language Processing Syllabus PDF

**Natural Language Processing** **Course Content:** **Unit I: 5 lecture hours** **Basics of Deep Learning:** Basic Concept of Natural Language Processing, Understanding of Text data and Speech data. Application of Natural Language Processing. **Unit II: 6 lecture hours** **Text Preprocessing T...

**Natural Language Processing** **Course Content:** **Unit I: 5 lecture hours** **Basics of Deep Learning:** Basic Concept of Natural Language Processing, Understanding of Text data and Speech data. Application of Natural Language Processing. **Unit II: 6 lecture hours** **Text Preprocessing Techniques:** Tokenization, Stemming, Lemmatization, Regular Expression, Bag of words, Tf-idf, N-grams, Bi-grams, Uni-grams. **Unit III: 10 lecture hours** **Text Classification:** **Basic use of Machine Learning in Natural Language processing, Word Embedding, CBOW, Skip-gram, Word2Vec. Probabilistic Learning:** Introduction to Probability, Conditional Probability, Bayesian Learning, Bayes Optimal classifier, Naïve Bayes Classifier. Sentiment Analysis. **Unit IV** **9 lecture hours** **POS Tagging:** Syntax analysis Part-of-Speech tagging, Rule base POS tagging, Stochastic POS tagging, Sequence labeling: Hidden Markov model. **Unit V 15 lecture hours** **Deep Learning in NLP:** Application of RNN in Text Data. Recurrent Neural Network, Variants of RNN: Longest Short term memory, Gated Recurrent Unit. Bidirectional RNN, Sequence to Sequence learning: Encoder and Decoder. Attention model architecture. Transformers.