1. To understand the vector representation of documents and apply cosine to measure similarity 2. To understand TF and IDF weighting and gain hands-on experience with vector space models 3. To understand how naïve Bayes classifier works for text classification 4. To gain some basic knowledge about other classification algorithms including linear classifiers and neural networks 5. To apply API for text classification and document clustering 6. To understand why topic modeling is useful and apply Gensim API to derive topics from a corpus 7. To understand the basic approaches to some typical problems in sentiment analysis and apply supervised approach for sentiment polarity classification 8. To gain some basic understanding of natural language processing 9. To understand Information Extraction (IE), techniques and its applications 10. To understand Named Entity Recognition (NER) and gain knowledge about the techniques for NER 11. To understand the definitions of accuracy, precision, recall and F-measure 12. To be aware of evaluation methods for text clustering 13. To understand advanced text analytics tasks like Text summarization and Question answering and apply techniques for such tasks To apply deep learning models and LLM models for text processing and mining tasks.