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From Learning to Leading.
Master Natural Language Processing (NLP)
Learn how to process, analyze, and generate human language using NLP techniques.
What you'll learn
This course covers the fundamentals and advanced techniques of NLP, including text processing, sentiment analysis, and deep learning applications.
- Text Preprocessing: Tokenization, stemming, lemmatization, and stopword removal.
- Text Representation: Bag-of-Words (BoW), TF-IDF, and word embeddings.
- Sentiment Analysis: Perform sentiment classification using machine learning.
- Named Entity Recognition (NER): Extract key entities from text.
- Deep Learning for NLP: Build models using LSTMs, Transformers, and BERT.
- Chatbots & Text Generation: Develop AI-powered conversational agents.
Gain hands-on experience with real-world NLP applications.
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Course Content
- What is NLP?
- Applications of NLP in the real world
- Overview of NLP Libraries (NLTK, SpaCy, Transformers)
- Tokenization, Stemming, Lemmatization
- Stopword Removal and Text Normalization
- Feature Extraction (BoW, TF-IDF, Word Embeddings)
- Text Classification using Logistic Regression, SVM, Naive Bayes
- Named Entity Recognition (NER)
- Sentiment Analysis with Scikit-Learn
- Introduction to LSTMs & GRUs
- Transformers & BERT
- Sequence-to-Sequence Models for Text Generation
- Building Chatbots with NLP
- Text Summarization & Translation
- Deploying NLP Models with Flask & FastAPI
Requirements
- Basic Python knowledge
- Familiarity with Machine Learning concepts
- Enthusiasm to work with text data
Description
- Learn core NLP techniques
- Apply deep learning to language tasks
- Build real-world NLP applications
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