Natural Language Processing with Probabilistic Models
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Natural Language Processing with Probabilistic Models

أبرز محتويات الدورة

In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

حول مقدم الدورة

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الطبع بواسطة

  • self
    التعلم الذاتي
  • dueration
    المدة 31 ساعات
  • domain
    الاختصاص علم البيانات والذكاء الاصطناعي
  • subs
    Monthly Subscription
    Course is included in
    1. الباقة الإبتدائية @ AED 99 + VAT
    2. الباقة الاحترافية @ AED 149 + VAT
  • fee
    Buy Now AED 170.99 + VAT
  • language
    اللغة الإنكليزية