Natural Language Processing with Sequence Models Course Syllabus

Overview: This course provides a comprehensive introduction to sequence models in Natural Language Processing, focusing on practical applications of recurrent neural networks and advanced architectures. Learners will gain hands-on experience building models for sentiment analysis, language generation, named entity recognition, and duplicate question detection. The course is structured into four technical modules and a final project, with an estimated total time commitment of 25 hours, making it ideal for working professionals seeking flexible, in-depth learning.

Module 1: Neural Networks for Sentiment Analysis

Estimated time: 5 hours

• Introduction to deep neural networks in NLP
• Word embeddings and their role in text representation
• Building a tweet classifier for sentiment polarity
• Evaluating model performance on real-world tweet data

Module 2: Recurrent Neural Networks for Language Modeling

Estimated time: 5 hours

• Limitations of traditional n-gram language models
• Architecture and mechanics of RNNs
• Implementing Gated Recurrent Units (GRUs)
• Generating synthetic text sequences using GRU models

Module 3: LSTMs and Named Entity Recognition

Estimated time: 5 hours

• Understanding the vanishing gradient problem in RNNs
• Long Short-Term Memory (LSTM) network architecture
• Applying LSTMs to extract named entities from text

Module 4: Siamese Networks for Duplicate Question Detection

Estimated time: 5 hours

• Concepts of semantic similarity in text
• Siamese LSTM architecture for sentence comparison
• Identifying duplicate questions in datasets

Module 5: Final Project

Estimated time: 5 hours

• Build an end-to-end NLP application using sequence models
• Apply learned techniques to a real-world dataset
• Submit model code and analysis for peer review

Prerequisites

• Basic proficiency in Python programming
• Familiarity with machine learning fundamentals
• Understanding of basic deep learning concepts

What You'll Be Able to Do After

• Train neural networks with word embeddings for sentiment analysis
• Generate synthetic text using GRU-based language models
• Implement Named Entity Recognition using LSTM networks
• Utilize Siamese LSTM networks to detect duplicate questions
• Apply sequence modeling techniques to real-world NLP tasks