NLP & LLM

NLP,

embeddings ,

transformers ,

Mastering RAG,

Building LLM Products,

Agents,

Gradio UI,

LLM Showdown: Evaluating Model,

Fine tuning Frontier LLMs using LoRA & QLoRA,

Build autonomous multi agent systems collaborating with models

1.introduction

Machine Learning Intuition, Python Data Types, Data Types Practical, List in Python, Tuples in Python, Dictionary in Python, Sets in Python

2. Fundamentals of Python Programming

Loops in Python, Comprehension in Python, Functions in Python, Lambda Functions in Python, Exception Handling in Python

3. Python Libraries-Numpy,Pandas,Matplotlib

Numpy Array Creation, Numpy Operations, Numpy Bonus Operations, Pandas in Python, Working with CSV Files, Pandas Operations, Pandas Bonus Operations, Matplotlib in Python, Regular Expressions in Python

4. Regular Expressions in Python

Regular Expressions in Python

5. Introduction to NLP-Challenges & Applications

NLP-Introduction, Challenges & Applications, Text Classification & Translation Pipeline, Text Clustering & Classification

6. Text Cleaning & Preprocessing using NLTK

Introduction to NLTK, Tokenization, Stop Word Removal, Stemming and Lemmatization, Named Entity Recognition, Introduction to POS Tagging, POS Tagging Architecture, POS Tagging Approach & Applications, Rare Word Removal, Correcting Words using NLTK

7. Text Cleaning and Preprocessing using SpaCy

Tokenization using SpaCy, POS Tagging using SpaCy, Dependency Parsing using SpaCy, Lemmatization using SpaCy, NER using SpaCy, Vector Similarity in SpaCy, NLTK vs SpaCy

8. Machine Learning Crash Course

Linear Regression, Logistic Regression, Support Vector Machine, K Nearest Neighbors Algorithm, Decision Trees, Random Forest

9. Text Representation Techniques in NLP

Bag of Words Technique, TF-IDF, Word Embeddings, Sentence Embeddings

10. Working with Text Files

Working with Text Files

11. Sentiment Analysis in NLP

What is Sentiment Analysis, Challenges, Applications & Takeaways, How Sentiment is Detected, Sentiment Analysis Implementation, Text Preprocessing for Sentiment Analysis, Text Cleaning Code, Sentiment Analysis with Machine Learning, ML for Sentiment Analysis Code, Training & Hyperparameter Tuning for Sentiment Analysis

12. ML Model Deployment using Streamlit

What is Model Deployment, Introduction to streamlit, Model Deployment using Streamlit

13. Multi Label text Classification

Multi Label Classification, Multi Label Binarization

14. Sentiment Analysis using WordVec Embeddings

Word2Vec Embeddings Data Preparation for Sentiment Analysis using Word2Vec Embeddings, Data preprocessing for sentiment analysis using word2vec embeddings

15. Emotion Recognition in Text using GloVe

Emotion Recognition in Text using GloVe

16. CV Parsing in NLP

What is CV Parsing in NLP, Implementation of CV Parsing using Spacy3

17. Sentiment Analysis using Deep Learning

What is Deep Learning, Machine Learning vs. Deep Learning, Sentiment Analysis using Deep Learning, Data Preparation for Sentiment Analysis using Deep Learning, Train Test Split for Sentiment Analysis Using Deep Learning, Artificial Neural Networks, Convolutional Neural Networks

18. Hate Speech Classification

Introduction to Hate Speech Classification, Workflow of Hate Speech Classification, Implementation of Hate Speech Classification, Testing in Hate Speech Classification

19. Poetry Generation in NLP

Understanding RNN and its types, Challenges of RNNs, Poetry Generation in NLP

20. Transformers

Introduction to Transformers, Encoder Decoder Architecture, Applications of Transformers, Pretrained Language Models - BERT & GPT

21. Prompt Engineering

Prompt Engineering - Techniques & Examples

22. Multi-modal NLP

Multi-modal NLP & Applications

23. Intro to Gen AI

Introduction to GEN AI

24. Capstone Project Hands-On

Capstone Project Hands-On

25. NLP - Conclusion

NLP - Conclusion, NLP - Future & Scope