Comprehensive course analysis
About the course
The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. NLP is undergoing rapid evolution as new methods and toolsets converge with an ever-expanding availability of data. In this course you will explore the fundamental concepts of NLP and its role in current and emerging technologies. You will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, you will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models for question answering, machine translation, and other language understanding tasks.
What you will learn
- Computational properties of natural languages
- Neural network models for language understanding tasks
- Word vectors, syntactic, and semantic processing
- Coreference, question answering, and machine translation
- College Calculus, Linear Algebra: You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.
- Basic Probability and Statistics: You should know basics of probabilities, gaussian distributions, mean, and standard deviation.
- Foundations of Machine Learning (recommended but not required): Knowledge of basic machine learning and/or deep learning is helpful, but not required.
Christopher Manning is a professor of computer science and linguistics at Stanford University and Director of the Stanford Artificial Intelligence Laboratory. He works on software that can intelligently process, understand, and generate human language material. He is a leader in applying Deep Lea...
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