Natural Language Processing Specialization

Natural Language Processing Specialization

Natural Language Processing Specialization Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October ’21. Taught in English 22 languages available Some content may not be translated Instructors: Łukasz Kaiser +2 more Close Instructors Łukasz Kaiser DeepLearning.AI 4 Courses • 194,260 learners Younes Bensouda Mourri DeepLearning.AI 5

Description

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. With the increasing amount of data available in textual form, the demand for professionals with expertise in NLP is on the rise. To meet this demand, several universities and online platforms now offer specialized courses and certifications in NLP.

One such option is the Natural Language Processing Specialization, which provides students with a comprehensive understanding of the theory, techniques, and applications of NLP. This specialization covers a wide range of topics, including language modeling, sentiment analysis, named entity recognition, machine translation, and more. Students also learn how to apply NLP techniques to real-world problems in areas such as healthcare, finance, and social media.

The Natural Language Processing Specialization typically consists of several courses that

Natural Language Processing Specialization

Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October ’21.

Taught in English

Some content may not be translated

Łukasz Kaiser
Younes Bensouda Mourri
Eddy Shyu

Instructors: Łukasz Kaiser

123,192 already enrolled

Specialization – 4 course series

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4.6

(5,343 reviews)

Intermediate level

Recommended experience

3 months at 10 hours a week
Flexible schedule
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What you’ll learn

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

  • Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

  • Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.

  • Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

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