Natural Language Processing (NLP) & Text Analytics
Learn Natural Language Processing (NLP) & Text Analytics with practical training in text processing, machine learning, word embeddings, transformers, and real-world NLP applications.
Course Overview
Course Modules Module 1: NLP Fundamentals Text Preprocessing Tokenization Stemming & Lemmatization Stop Words Regular Expressions Text Representation Module 2: Classical NLP Techniques Bag of Words TF-IDF Naive Bayes Classification Named Entity Recognition POS Tagging Dependency Parsing Module 3: Word Embeddings & Representations Word2Vec GloVe FastText Sentence Embeddings Semantic Similarity Vector Space Models Module 4: Deep Learning for NLP RNNs & LSTMs Attention Mechanism Transformer Architecture BERT & GPT Fine-tuning Pre-trained Models HuggingFace Library Module 5: NLP Applications Sentiment Analysis Text Classification Machine Translation Summarization Question Answering Chatbot Development Module 6: Capstone Project Problem Scoping Data Collection Model Development Fine-tuning Evaluation API Deployment Capstone Project Build an NLP pipeline for a real-world text classification or sentiment analysis problem, deployed as an API.
Learning Outcomes
Apply classical and deep learning NLP techniques.
Build and fine-tune transformer-based language models.
Implement NLP applications like sentiment analysis and chatbots.
Work with HuggingFace and spaCy libraries.
Deploy NLP models as production services.
Prerequisites
Participants should have: Python programming skills Foundational machine learning concepts Basic linear algebra and statistics
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