From Basics of NLP to ChatGPT and Beyond -->

Sharing detailed list of NLP topics to get in-depth knowledge;

1. NLP kick-start:

• Text cleaning process, RegEx, NLTK and Spacy toolkit.
• Feature extraction: tokenization, bag of words, tf-idf, word embeddings.
• Basic NLP tasks: Sentiment Analysis, Text Classification, Topic modelling, Named entity recognition. (This can be solved with basic NLP and ML knowledge only)

To deep-dive further in NLP -->

2. Embeddings:

What I feel is you should consider embeddings part in-dept. word2vec, glove etc

3. Deep Learning back-bone models:

CNN, RNN, LSTM, GRU models, Please consider Seq2Seq(Encoder-Decoder) models, latest Large language models are highly depend on Seq2Seq mechanism.

4. GenAI and LLM:

• Introduction of GenAI
• Attention Mechanism - Self-Attention, Multi-Head Attention
• Transformer Model
• Variants of Transformer models - BERT(Encoder part), GPT(Decoder part), Flan-T5(Encoder-Decoder)
• ChatGPT: GPT, reinforcement learning, reward model.
• Training for LLM models - What is Transfer Learning, Pre-training and Fine-Tuning for specific tasks
• Inference part - Prompt Engineering, In-Context learning.
• Model Evaluation and Benchmarking
• Parameter Efficient Fine-tuning (quite effective when we've computation power limitations)

NLP Tasks and Materials: (In-Progress)

1. In-Dept Text Classification - Kaggle competition : Feedback Prize - Evaluating Student Writing

• Kaggle Notebook: https://www.kaggle.com/shwetagargade/detailed-understanding-of-text-classification

2. Tweeter Scrapping:

• Kaggle Notebook: https://www.kaggle.com/shwetagargade/tweeter-scrapping

3. Fun with NLP API's:

• Kaggle Notebook: https://www.kaggle.com/shwetagargade/fun-nlp-with-api-s

4. Learn NLP Libraries:

• Kaggle Notebook: https://www.kaggle.com/shwetagargade/learn-nlp-libraries

5. Recommendation Systems:

• Medium Blog: https://medium.com/@shwetagargade216/recommendation-systems-6be5c12e2aa5