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Welcome to my NLP class notes
1. Open Source AI
2. Introduction to Text Analytics
3. Basic Text Handling
4. Regular Expressions
5. Using NLTK, more Text Handling, and Entity Extraction
6. Linear Algebra, Gradients, Optimization
7. Linear Algebra, Eigensystems, Decompositions
8. Machine Learning: A Quick Introduction
9. Parts of Speech (POS) Tagging
10. Using AWS Comprehend with IAM Role
11. Web-Scraping, Dictionaries, Sentiment
12. News Scraping with Python
13. Using Tweets
14. Using Amazon Textract
15. S3 and Textract
16. Deep Learning: Introduction
17. PyTorch Basics and Neural Nets
18. Hugging Face for Sentiment, Q&A
19. Text Transformations, Numbers, Punctuation, Stopwords, Stemming, Corpus
20. Term Document Matrix
21. Document Similarity
22. Text Comprehension
23. Summarization
24. Text Classification with GLMNet
25. Text Classification with FastText
26. Text Classification with spaCy
27. Text Classification with AutoGluon
28. Topic Modeling
29. Dimension Reduction (and Topics)
30. Embeddings
31. t-distributed Stochastic Neighbor Embedding
32. Generalized Language Models
33. Using Hugging Face LLMs
34. Chronos forecasting
35. Natural Language Generation (NLG)
36. Chaining LLMs with
LangChain
37. Retrieval Augmented Generation (RAG)
38. Model Context Protocol (MCP)
39. LangGraph and Agentic Workflows
40. Multi-agent Systems
41. PocketFlow
42. Streamlit
43. Shiny-Python Basics
44. Prompting
45. Reinforcement Learning
46. Reinforcement Learning with Human Feedback (RLHF)
47. Enhancing Foundation Models
48. Explaining a ML Model using Shapley Values
49. Algorithmic Fairness
50. AWS SageMaker JumpStart
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Index
