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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) with
LangChain
38. LangGraph and Agentic Workflows
39. Multi-agent Systems
40. Streamlit
41. Shiny-Python Basics
42. Prompting
43. Reinforcement Learning
44. Reinforcement Learning with Human Feedback (RLHF)
45. Enhancing Foundation Models
46. Explaining a ML Model using Shapley Values
47. Algorithmic Fairness
48. AWS SageMaker JumpStart
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