Natural Language Processing
Backbone of autonomous agents and human-machine discovery.
Our NLP (Natural Language Processing) online course is designed to take you from foundational concepts to industry-ready expertise with a clear and structured learning path. You will learn core topics such as text preprocessing, tokenization, stemming, lemmatization, POS tagging, and language modeling, followed by advanced concepts like word embeddings, transformers, sentiment analysis, chatbots, and real-world NLP applications. The course includes live interactive classes, recorded sessions, hands-on practice tasks, and complete mentor support to help you gain strong practical and implementation skills. It is ideal for beginners, students, and working professionals who want to build a successful career in AI and language-based technologies.
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Course Details
3 Months
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Natural Language Processing
Module 1: NLP Foundations (6 Hours)
- Hour 1: Introduction to NLP, applications, key challenges (ambiguity, context)
- Hour 2: Text data basics, corpora, tokens, vocabulary, NLP pipeline
- Hour 3: Text preprocessing: tokenization, stopwords, stemming vs lemmatization
- Hour 4: Bag of Words & TF-IDF, hands-on document similarity
- Hour 5: Word embeddings (Word2Vec, GloVe), hands-on pretrained embeddings
- Hour 6: Mini Project 1: Spam detection using TF-IDF + Logistic Regression
Module 2: Core NLP Tasks (8 Hours)
- Hour 7: Language modeling, N-grams, Markov models
- Hour 8: POS tagging & NER using NLTK and spaCy (hands-on)
- Hour 9: Sentiment analysis with IMDb reviews dataset
- Hour 10: Topic Modeling with LDA, hands-on with news articles
- Hour 11: Sequence labeling using CRFs (hands-on)
- Hour 12: Text similarity & clustering using embeddings
- Hour 13: NLP evaluation metrics: BLEU, ROUGE, precision/recall/F1
- Hour 14: Mini Project 2: End-to-end tweet sentiment analysis
Module 3: Deep Learning for NLP (8 Hours)
- Hour 15: RNNs, LSTMs, GRUs for text
- Hour 16: Hands-on: Text classification using LSTM
- Hour 17: Attention mechanism in NLP
- Hour 18: Seq2Seq & machine translation (hands-on)
- Hour 19: Transformers: BERT, GPT, T5
- Hour 20: Fine-tuning BERT with Hugging Face
- Hour 21: NER using Transformers (hands-on)
- Hour 22: Mini Project 3: Text summarization
Module 4: Advanced Topics & Real-World NLP (5 Hours)
- Hour 23: Question Answering systems (extractive & generative)
- Hour 24: Conversational AI & chatbots (Rasa / HF)
- Hour 25: Speech NLP: ASR & TTS overview
- Hour 26: Bias, ethics & hallucinations in LLMs
- Hour 27: Project 4: Domain-specific chatbot
Module 5: Capstone Project & Wrap-up (3 Hours)
- Hour 28: Capstone kickoff & dataset selection
- Hour 29: End-to-end NLP pipeline & deployment
- Hour 30: Presentations, feedback & future of NLP (LLMs, RAG, MLOps)