Natural Language Processing with Classification and Vector Spaces

Enroll hereIn the Natural Language Processing with Classification and Vector Spaces, one of the best online logistic regression courses, is first courses, you will: Use logistic regression and then nave Bayes to analyze the sentiment of tweets, Use vector space models to discover relationships between words and then use PCA to reduce the dimensionality of the vector space and visualize those relationships, Write a simple English to French translation algorithm that uses pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search.

You'll have produced NLP apps that conduct question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot by the end of this Specialization! Two experts in natural language processing, machine learning, and deep learning devised and taught this Specialization. Younes Bensouda Mourri is a Stanford University AI Instructor who also assisted in the development of the Deep Learning Specialization. ukasz Kaiser is a Google Brain Staff Research Scientist and co-author of Tensorflow, the Tensor2Tensor and Trax libraries, as well as the Transformer paper.

LEARN STEP BY STEP:

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.

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Enroll here: coursera.org/learn/classification-vector-spaces-in-nlp