Natural Language Processing

Turn Human Language Into Intelligent Data-driven Applications

Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. This course provides a comprehensive introduction to NLP, from traditional text processing methods to modern deep learning and transformer-based architectures.
You will learn essential techniques such as text preprocessing, tokenization, lemmatization, and word embeddings, followed by the implementation of neural network models including RNNs, LSTMs, and transformer architectures. Through hands-on projects, you will design and deploy end-to-end NLP pipelines for real-world applications such as text classification, document similarity, and language modeling.

By the end of the course, you will be equipped with the skills and tools needed to solve complex NLP problems using state-of-the-art machine learning techniques.

Course Highlights:

• Hands-on Python programming for NLP using Jupyter Notebooks or Google Colab
• Text preprocessing: tokenization, normalization, stemming, and lemmatization
• Word embeddings and document representation (TF-IDF, Word2Vec, GloVe)
• Deep learning models: RNNs, LSTMs, and transformer architectures
• Building and deploying end-to-end NLP pipelines
• Practical applications in text analysis and language modeling

Course Benefits

Upon successful completion of this course, you will be able to:

• Build a strong foundation in Natural Language Processing and AI
• Gain practical experience with modern NLP tools and frameworks
• Learn how to design and train neural networks for language tasks
• Apply NLP techniques to real-world problems and datasets
• Prepare for careers in artificial intelligence and data science
• Apply credit earned toward an academic degree or professional credential, subject to the approval of the receiving institution(s)

Course Details and Next Steps

• Course Typically Offered: Online in Winter and Summer quarters
• Prerequisites: CSE-40028 Introduction to Programming (Python) or equivalent practical experience, and linear algebra, probability and statistics skills
• Next Step: After completing this course, consider taking other courses in the Machine Learning Methods, Technical Aspects of Artificial Intelligence or Python Programming certificate
• Contact: For more information about this cousre, please contact us at unex-techdata@ucsd.edu