🎓 Course Modules:
📦 Module 1: Introduction to NLP
What is NLP and why it matters
NLP in real-world applications
Differences between NLP, NLU, and NLG
🔧 Module 2: Text Preprocessing Techniques
Tokenization, stop word removal
Stemming and lemmatization
Case normalization and cleaning pipelines
🧠 Module 3: Part-of-Speech Tagging & Named Entity Recognition
Understanding POS tagging with examples
Named entities: people, locations, organizations
Rule-based vs. machine learning approaches
🔍 Module 4: Vectorization & Word Embeddings
Bag-of-Words and TF-IDF
Word2Vec and GloVe embeddings
Contextual embeddings (BERT basics)
💬 Module 5: Sentiment Analysis & Text Classification
Binary and multi-class text classification
Sentiment detection in reviews/social media
Using scikit-learn and spaCy
🧰 Module 6: NLP with Deep Learning
RNNs and LSTMs for sequence modeling
Transformers and attention mechanisms
Introduction to BERT and GPT architectures
🌐 Module 7: Language Modeling & Text Generation
N-gram and probabilistic models
Neural language models
Generative text applications with GPT
🗣️ Module 8: NLP for Speech and Chatbots
Speech-to-text and intent recognition
Dialog systems and chatbot design
Tools: Dialogflow, Rasa, Hugging Face
🛠️ Module 9: NLP Tools & Libraries
NLTK, spaCy, Hugging Face Transformers
TextBlob, Gensim, OpenAI APIs
Choosing the right tool for your task
⚖️ Module 10: Ethics, Bias, and Responsible NLP
Language bias and fairness in AI
Data privacy and cultural sensitivity
Building inclusive NLP models
✅ Module 11: Capstone Project
Build your own NLP pipeline from scratch
Choose between sentiment analysis, chatbot, or text generation
Final evaluation and peer review
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
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