1. Introduction to NLP:

   • Definition of NLP
   • Importance and applications of NLP
   • Evolution and history of NLP

2. Text Preprocessing:

   • Tokenization
   • Stopword removal
   • Stemming and Lemmatization
   • Part-of-speech (POS) tagging

3. Text Representation:

   • Bag-of-Words (BoW) model
   • TF-IDF (Term Frequency-Inverse Document Frequency)
   • Word Embeddings (Word2Vec, GloVe, etc.)
   • Contextual embeddings (BERT, GPT, etc.)

4. Text Classification:

   • Binary and multiclass classification
   • Naive Bayes Classifier
   • Support Vector Machines (SVM)
   • Neural Networks for text classification (CNN, RNN, LSTM)

5. Named Entity Recognition (NER):

   • Definition and importance
   • Approaches to NER (rule-based, machine learning, deep learning)
   • Evaluation metrics (Precision, Recall, F1-score)

6. Sentiment Analysis:

   • Introduction to sentiment analysis
   • Sentiment polarity (positive, negative, neutral)
   • Approaches to sentiment analysis (lexicon-based, machine learning, deep learning)

7. Language Modeling:

   • Introduction to language modeling
   • N-gram models
   • Recurrent Neural Networks (RNNs) for language modeling
   • Transformer architecture for language modeling

8. Text Generation:

   • Introduction to text generation
   • Markov Chain text generation
   • Recurrent Neural Networks (RNNs) for text generation
   • Generative Pre-trained Transformer (GPT) models for text generation

9. Machine Translation:

   • Introduction to machine translation
   • Statistical Machine Translation (SMT)
   • Neural Machine Translation (NMT)
   • Evaluation metrics for machine translation (BLEU, METEOR, etc.)

10. Topic Modeling:

    • Introduction to topic modeling
    • Latent Dirichlet Allocation (LDA)
    • Non-negative Matrix Factorization (NMF)
    • Evaluation of topic models

11. Text Summarization:

    • Introduction to text summarization
    • Extractive summarization
    • Abstractive summarization
    • Evaluation metrics for summarization (ROUGE, BLEU, etc.)

12. Question Answering:

    • Introduction to question answering
    • Information retrieval-based QA
    • Knowledge-based QA
    • End-to-end machine learning-based QA

13. Text Similarity and Clustering:

    • Introduction to text similarity
    • Cosine similarity
    • Hierarchical clustering
    • K-means clustering

14. Advanced NLP Techniques:

    • Coreference resolution
    • Dependency parsing
    • Discourse analysis
    • Emotion detection in text

15. Ethical and Social Implications of NLP:

    • Bias and fairness in NLP models
    • Privacy concerns
    • Misuse of NLP technology
    • Responsible AI practices in NLP