From NLTK to BERT: Build Your First AI Sentiment Model (IMDB Dataset + Python Tutorial)

In this video, you will learn how to build a complete Sentiment Analysis system using BERT (Bidirectional Encoder Representations from Transformers) from scratch.

We start with the basics of NLP (Natural Language Processing), understand the limitations of traditional libraries like NLTK, and then move towards modern deep learning models like BERT.

What you will learn:
- What is NLP and why it is important
- Difference between NLTK and BERT
- How Tokenizer works (Text → Numbers)
- How Dataset is used for training
- Training a BERT model using IMDB dataset
- Understanding model predictions & confidence scores
- Saving and loading models locally (No repeated downloads)
- Real-time user input sentiment prediction

Advanced Concepts Covered:
- Tokenization (input_ids, attention_mask, token_type_ids)
- Truncation issues and how to fix them
- Model confidence interpretation
- Domain Adaptation (Finance, Medical, Legal, Social datasets)

Datasets you can try:
- IMDB (Movie Reviews)
- Financial PhraseBank (Finance)
- TweetEval (Social Media)
- LexGLUE (Legal)
- PubMed (Medical)

🛠 Tech Stack:
- Python
- PyTorch
- HuggingFace Transformers
- Datasets Library

Code includes:
✔ Training pipeline
✔ Evaluation metrics (Accuracy + F1 Score)
✔ Local caching system
✔ Interactive user input system

This tutorial is perfect for:
- Beginners in NLP
- Students learning AI/ML
- Developers building real-world AI applications

#NLP #BERT #MachineLearning #DeepLearning #Python #AI #HuggingFace #SentimentAnalysis

Видео From NLTK to BERT: Build Your First AI Sentiment Model (IMDB Dataset + Python Tutorial) канала Swarup Kumar Saha