Introduction to NLP
Learn the fundamentals of how machines understand and process human language.
What is NLP?
Definition, applications, and importance in AI.NLP vs. Traditional Text Processing
Key differences and why NLP is transformative.Core Challenges in NLP
Ambiguity, context, slang, and multilingual processing.
Beginner NLP Tutorials
Get started with hands-on NLP projects.
1. Text Preprocessing Techniques
Tokenization (word & sentence)
Stopword removal
Stemming & Lemmatization
Part-of-Speech (POS) Tagging
2. Basic NLP Tasks
Sentiment Analysis (Positive/Negative/Neutral classification)
Named Entity Recognition (NER) (Identifying names, places, dates)
Text Classification (Spam detection, topic labeling)
3. Working with NLP Libraries
NLTK (Natural Language Toolkit) – Classic Python library for NLP
spaCy – Fast and efficient NLP processing
TextBlob – Simple sentiment analysis and translation
Intermediate NLP Concepts
Dive deeper into language models and advanced techniques.
1. Word Embeddings & Vectorization
Bag-of-Words (BoW)
TF-IDF (Term Frequency-Inverse Document Frequency)
Word2Vec, GloVe, and FastText
2. Sequence Modeling
Recurrent Neural Networks (RNNs) for NLP
Long Short-Term Memory (LSTM) networks
Gated Recurrent Units (GRUs)
3. Transformers & Attention Mechanisms
Introduction to the Transformer architecture
How self-attention works
BERT, GPT, and other pre-trained models
Advanced NLP Applications
Build cutting-edge NLP systems.
1. Large Language Models (LLMs)
Fine-tuning GPT, BERT, and T5
Prompt engineering techniques
Retrieval-Augmented Generation (RAG)
2. Speech & Language Generation
Text-to-Speech (TTS) systems
Chatbots & Conversational AI
Abstractive vs. Extractive Summarization
3. Multilingual & Low-Resource NLP
Handling languages with limited datasets
Zero-shot and few-shot learning
Machine Translation (e.g., Google Translate, MarianMT)
Tools & Frameworks
Popular libraries and platforms for NLP development.
Hugging Face Transformers – Access pre-trained models
LangChain – Build LLM-powered applications
OpenAI API – Integrate GPT models into apps
Stanford CoreNLP – Robust Java-based NLP toolkit
NLP Project Ideas
Practice with real-world applications.
✔ Build a Twitter Sentiment Analyzer
✔ Create a Resume Parser for Job Applications
✔ Develop a Question-Answering Chatbot
✔ Train a Custom Text Summarizer
✔ Language Translation Tool
