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Data Sciences

Natural Language Processing-Master Class

ratings 

This course is about the learning process of Natural language processing, NLP contend to build machines that understand and answer to text or voice data in much the same way humans do.

PRIVATE
Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

Natural language processing describes the interaction between human language and computers. It’s a technology that many people use daily and has been around for years. Natural language processing refers to the branch of artificial intelligence, which gives computers the ability to understand text and spoken words in much the same way human beings can. People are great at producing language, understanding language and are capable of expressing and perceiving very complicated meanings. At the end of this course, you will know:
  • What natural language is and how it is different from other types of data.
  • Natural Language Processing with advance deep learning
  • What makes working with natural language so challenging

Course Curriculum

    • Computational Linguistic 00:00:00
    • History of NLP? 00:00:00
    • Why NLP 00:00:00
    • NLP Use cases 00:00:00
    • Tokenization 00:00:00
    • Lemmatization 00:00:00
    • Stemming 00:00:00
    • Stop Words 00:00:00
    • Labs 00:00:00
    • Bag Of Words 00:00:00
    • TFIDF 00:00:00
    • Unigrams 00:00:00
    • Bigrams 00:00:00
    • N-grams 00:00:00
    • Gensim 00:00:00
    • Word2Vec 00:00:00
    • AvgWord2Vec 00:00:00
    • Labs 00:00:00
    • Spacy and NLTK Overview 00:00:00
    • Spacy & NLTK Function Implementation in text processing 00:00:00
    • POS tagging, Challenges and accuracy 00:00:00
    • Name and Entities Recognition 00:00:00
    • Word embedding 00:00:00
    • Word2Vec 00:00:00
    • Labs 00:00:00
    • Neural Network overview and its use case 00:00:00
    • Use of Neural Network in NLP 00:00:00
    • Multilayer Network 00:00:00
    • Loss Functions 00:00:00
    • The Learning Mechanism 00:00:00
    • Optimizers 00:00:00
    • Forward and Backward Propagation 00:00:00
    • Gradient Descent 00:00:00
    • Introduction to RNN 00:00:00
    • Problem with RNN 00:00:00
    • Understanding LSTM 00:00:00
    • Exploding Gradient Problem 00:00:00
    • How to solve Exploding Gradient problem 00:00:00
    • Vanishing Gradient Problem 00:00:00
    • How to solve Vanishing Gradient problem 00:00:00
    • Hands on 00:00:00
    • What is Bidirectional RNN 00:00:00
    • Bidirectional RNN In-depth Intuition 00:00:00
    • Hands on 00:00:00
    • What is Encoder and Decoder 00:00:00
    • In-depth Intuition of Encoder and Decoder 00:00:00
    • Problems with Encoders and Decoder In-depth Intuition 00:00:00
    • Hands on 00:00:00
    • What is Attention Models 00:00:00
    • In-depth Intuition of Attention Models 00:00:00
    • Hands on 00:00:00
    • What are Transformers 00:00:00
    • Transformers In-depth Intuition 00:00:00
    • Hands on 00:00:00
    • What is Bert 00:00:00
    • In-depth Intuition on Bert 00:00:00
    • Hands on 00:00:00

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