{"id":5616,"date":"2020-07-02T08:00:39","date_gmt":"2020-07-02T02:30:39","guid":{"rendered":"https:\/\/www.upnxtblog.com\/?p=5616"},"modified":"2021-02-26T14:01:09","modified_gmt":"2021-02-26T08:31:09","slug":"natural-language-processing-specialization-from-deeplearning-ai","status":"publish","type":"post","link":"https:\/\/www.upnxtblog.com\/index.php\/2020\/07\/02\/natural-language-processing-specialization-from-deeplearning-ai\/","title":{"rendered":"Natural Language Processing Specialization from deeplearning.ai"},"content":{"rendered":"
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We have already looked at TOP 100 Coursera Specializations<\/a><\/strong> and today we will check out Natural Language Processing Specialization from deeplearning.ai<\/p>\n

Coursera Specialization<\/strong> is a series of courses that help you master a skill. To begin, you can enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you\u2019re automatically subscribed to the full Specialization. You can either complete just one course or you can pause your learning or end your subscription at any time.<\/p>\n

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning.\u00a0Younes Bensouda Mourri<\/a>\u00a0is an Instructor of AI at Stanford University who also helped build the\u00a0Deep Learning Specialization<\/a>.\u00a0\u0141ukasz Kaiser<\/a>\u00a0is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.<\/p>\n

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.<\/p>\n

This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:<\/strong><\/p>\n

\u2022 Use logistic regression, na\u00efve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words, and use locality sensitive hashing for approximate nearest neighbors.<\/p>\n

\u2022 Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.<\/p>\n

\u2022 Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.<\/p>\n

\u2022 Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering and to build chatbots. Models covered include T5, BERT, transformer, reformer, and more!<\/p>\n

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Click to Enroll!<\/figcaption><\/figure>\n

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What will you learn<\/h2>\n