Natural Language Processing with Probabilistic Models

Overview

Embark on a transformative journey with the "Natural Language Processing with Probabilistic Models" course, the second installment in the renowned Natural Language Processing Specialization offered through Coursera. This comprehensive course empowers you to:

• Develop a practical auto-correct tool by leveraging minimum edit distance and dynamic programming.
• Master the application of the Viterbi Algorithm for accurate part-of-speech tagging, a cornerstone of computational linguistics.
• Enhance your skills in crafting advanced auto-complete algorithms using the powerful N-gram language model.
• Innovate with your very own Word2Vec model, employing a neural network to generate meaningful word embeddings through a continuous bag-of-words approach.

By completing this specialization, you'll gain the expertise to create NLP applications capable of question-answering and sentiment analysis, devise language translation and text summarization tools, and even design a sophisticated chatbot.

This specialization is meticulously crafted by NLP, machine learning, and deep learning luminaries, Younes Bensouda Mourri and Łukasz Kaiser. Mourri, a distinguished Instructor of AI at Stanford University and a contributor to the Deep Learning Specialization, joins forces with Kaiser, a Staff Research Scientist at Google Brain, co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the seminal Transformer paper.

Dive into the realms of Natural Language Processing (NLP), Sentiment Analysis, and more with this cutting-edge course, available now on Coursera.

Taught by

Younes Bensouda Mourri, Łukasz Kaiser and Eddy Shyu

Subjects