Natural Language Processing Fundamentals Techniques Tools and Applications

Natural Language Processing Fundamentals Techniques Tools and Applications

Name	Natural Language Processing Fundamentals Techniques, Tools, and Applications	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.1 GB
Total Files	45
Hash	14EC94C6FA7B5CFC87C42FA1504560536AE821C3

/Module 2 Text Processing and Feature Engineering/
003. Lesson 2.3 Use Case Who Wrote it Authorship Attribution with Heuristic NLP.mp4	130.0 MB
001. Lesson 2.1 Preprocessing Techniques.en.srt	16.2 KB
001. Lesson 2.1 Preprocessing Techniques.mp4	62.7 MB
002. Lesson 2.2 Feature Engineering Techniques.en.srt	20.5 KB
002. Lesson 2.2 Feature Engineering Techniques.mp4	70.5 MB
003. Lesson 2.3 Use Case Who Wrote it Authorship Attribution with Heuristic NLP.en.srt	22.4 KB
/Module 0 Course Introduction/
001. Course Introduction.mp4	30.2 MB
001. Course Introduction.en.srt	7.5 KB
/Module 1 Introduction to Linguistics, NLP and Related Platforms/
001. Lesson 1.1 What is Linguistics.en.srt	14.0 KB
001. Lesson 1.1 What is Linguistics.mp4	49.5 MB
002. Lesson 1.2 What is Natural Language Processing.en.srt	15.0 KB
002. Lesson 1.2 What is Natural Language Processing.mp4	48.7 MB
003. Lesson 1.3 Major Platforms and Packages Used in this Course.en.srt	8.0 KB
003. Lesson 1.3 Major Platforms and Packages Used in this Course.mp4	52.7 MB
/Module 3 Traditional NLP Models/
001. Lesson 3.1 Probabilistic Models.en.srt	20.5 KB
001. Lesson 3.1 Probabilistic Models.mp4	77.5 MB
002. Lesson 3.2 Introduction to Neural Networks in NLP.en.srt	16.1 KB
002. Lesson 3.2 Introduction to Neural Networks in NLP.mp4	60.4 MB
003. Lesson 3.3 Use Case Text Classification with Naive Bayes.en.srt	12.3 KB
003. Lesson 3.3 Use Case Text Classification with Naive Bayes.mp4	61.0 MB
/Module 4 Advanced NLP Models/
001. Lesson 4.1 Attention Mechanism and Transformers.en.srt	12.0 KB
001. Lesson 4.1 Attention Mechanism and Transformers.mp4	47.4 MB
002. Lesson 4.2 Task-Specific Transformer Models and Fine-Tuning.en.srt	15.2 KB
002. Lesson 4.2 Task-Specific Transformer Models and Fine-Tuning.mp4	57.8 MB
003. Lesson 4.3 Large Language Models (LLMs).en.srt	19.1 KB
003. Lesson 4.3 Large Language Models (LLMs).mp4	63.9 MB
004. Lesson 4.4 Use Case Fine-tuning a Sentiment Analysis Classifier with DistilBERT.en.srt	20.6 KB
004. Lesson 4.4 Use Case Fine-tuning a Sentiment Analysis Classifier with DistilBERT.mp4	115.6 MB
/Module 5 Beyond the NLP Model/
001. Lesson 5.1 Ethical Considerations.en.srt	14.6 KB
001. Lesson 5.1 Ethical Considerations.mp4	43.5 MB
002. Lesson 5.2 MLOps and LLMOps.en.srt	13.7 KB
002. Lesson 5.2 MLOps and LLMOps.mp4	46.0 MB
003. Lesson 5.3 Use Case Build Your Own Retrieval-Augmented Generation (RAG) System.en.srt	15.6 KB
003. Lesson 5.3 Use Case Build Your Own Retrieval-Augmented Generation (RAG) System.mp4	85.7 MB
004. Lesson 5.4 Course Closing.en.srt	3.5 KB
004. Lesson 5.4 Course Closing.mp4	15.7 MB
/z.nlp-fundamentals-main/
0203_heuristic_nlp.ipynb	443.0 KB
0303_naive_bayes.ipynb	128.4 KB
0404_finetuning_transformers.ipynb	256.3 KB
0504_llms_and_rag.ipynb	121.8 KB
README.md	2.9 KB
/graphics/
cosine_similarity.png	23.0 KB
llm.png	25.7 KB
rag.png	112.6 KB
transformer_architecture.png	80.7 KB
Total files 45