Natural Language Processing (NLP)

data-science.

🎓 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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