Natural Language Processing
Learning Outcomes:
- Explain and apply fundamental linguistics ideas and methods to natural language processing.
- Construct a suitable pipeline for a particular NLP task and build pipeline components.
- Make Use Of linguistic concepts and methods to solve language challenges and develop algorithmic solutions to specific language difficulties.
Topics:
- Introduction to Language Model
- Introduction to NLP
- Preprocessing Technique
- Sentiment Analysis and Machine Learning in NLP
- Word Embedding
- Reccurent Neural Networks
- Convolutional Neural Networks
- Transfer Learning and Name Entity Recognition
- Semi Supervised and Active Learning
- Natural Language Understanding and Generating
Practicum:
- Statistical NLP
- Bag of Word – TF IDF
- Sentiment Analysis
- Deep Learning for NLP
- Transfer Learning
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