Natural Language Processing with Classification and Vector Spaces

via Coursera

Overview

Embark on an enlightening journey through the domain of Natural Language Processing (NLP) with the first course of the NLP Specialization. This comprehensive course is tailored to introduce you to the fascinating world of NLP through hands-on applications and essential techniques. You will learn how to:

Analyze sentiments from tweets utilizing advanced algorithms such as logistic regression and naïve Bayes,
Explore the intricate relationships between words with vector space models, apply Principal Component Analysis (PCA) to simplify the complexity of vector spaces, and visualize word relationships,
Develop a basic English to French translation algorithm leveraging pre-computed word embeddings and locality-sensitive hashing for approximate k-nearest neighbor searches.

Upon completing this Specialization, your skill set will be enriched with the capability to develop NLP applications for question-answering and sentiment analysis, craft tools for language translation and text summarization, and even create a responsive chatbot. This Specialization is meticulously curated and delivered by NLP and machine learning luminaries, Younes Bensouda Mourri and Łukasz Kaiser. Mourri, a distinguished Instructor of AI at Stanford University, has made significant contributions to the Deep Learning Specialization. Kaiser, a respected Staff Research Scientist at Google Brain, has co-authored the groundbreaking Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper. Offered through Coursera, this course falls under the essential categories of Natural Language Processing (NLP) Courses and Sentiment Analysis Courses, setting a solid foundation for learners aiming to delve deep into the world of NLP.