Natural Language Processing with Probabilistic Models Course Syllabus

Overview: This course provides a comprehensive introduction to probabilistic models in Natural Language Processing, combining theoretical foundations with hands-on implementation. Learners will explore key algorithms and models used in real-world NLP applications such as autocorrect, part-of-speech tagging, autocomplete, and word embeddings. The curriculum is structured into four core modules followed by a final project, totaling approximately 28 hours of flexible learning. Each module blends conceptual understanding with programming exercises, enabling practitioners to build and evaluate probabilistic systems effectively. Ideal for professionals seeking to deepen their NLP expertise with practical, model-driven techniques.

Module 1: Autocorrect

Estimated time: 6 hours

• Introduction to spell checking and error detection
• Minimum edit distance computation
• Dynamic programming for efficient corrections
• Implementing an autocorrect system

Module 2: Part of Speech Tagging and Hidden Markov Models

Estimated time: 5 hours

• Foundations of Markov chains
• Hidden Markov Models for sequence labeling
• Part-of-speech tagging with HMMs
• Viterbi algorithm for decoding tag sequences

Module 3: Autocomplete and Language Models

Estimated time: 8 hours

• N-gram language models
• Probability estimation for word sequences
• Smoothing techniques for sparse data
• Building an autocomplete system

Module 4: Word Embeddings with Neural Networks

Estimated time: 9 hours

• Introduction to distributed word representations
• Continuous Bag-of-Words (CBOW) model
• Neural network training for word embeddings
• Word2Vec and semantic similarity

Module 5: Final Project

Estimated time: 8 hours

• Design and implement a probabilistic NLP application
• Integrate components such as language models or embeddings
• Submit and evaluate the final system

Prerequisites

• Proficiency in Python programming
• Familiarity with basic machine learning concepts
• Understanding of fundamental probability and linear algebra

What You'll Be Able to Do After

• Implement autocorrect algorithms using minimum edit distance and dynamic programming
• Apply Hidden Markov Models and the Viterbi algorithm for part-of-speech tagging
• Develop N-gram language models for autocomplete functionalities
• Build Word2Vec models to generate word embeddings using neural networks
• Apply probabilistic models to real-world NLP tasks with confidence