Week 1-2: Introduction to NLP and Python basics
Overview of NLP applications and techniques
Introduction to Python and popular libraries (e.g., NumPy, Pandas)
Week 3-4: Text preprocessing and analysis
Text preprocessing techniques (e.g., tokenization, stemming, lemmatization)
Exploratory data analysis (EDA) on text data
Basic feature extraction (e.g., bag-of-words, TF-IDF)
Week 5-6: Supervised learning for NLP
Introduction to supervised learning algorithms (e.g., Naive Bayes, logistic regression, SVM)
Text classification and sentiment analysis
Evaluation metrics and cross-validation
Week 7-8: Unsupervised learning for NLP
Introduction to unsupervised learning algorithms (e.g., clustering, topic modeling)
Text clustering and topic modeling
Evaluation metrics for unsupervised learning
Week 9-10: Deep learning for NLP
Introduction to deep learning and neural networks
Word embeddings and language models (e.g., Word2Vec, GloVe, BERT)
Text generation and sequence-to-sequence models
Week 11-12: Advanced NLP topics and project work
Advanced NLP techniques (e.g., named entity recognition, coreference resolution)
Group or individual projects to apply NLP techniques to a real-world dataset