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    • U and P Artificial Intelligence Natural Language Processing with Python

    U&P AI - Natural Language Processing (NLP) with Python

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    Udemy

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    Lavel

    Beginner

    Mode

    Online

    Fees

    ₹ 1499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 1,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Getting an Idea of NLP and its Applications

    • Note!
    • Introduction to NLP
    • By The End Of This Section
    • Installation
    • Tips
    • U - Tokenization
    • P - Tokenization
    • U - Stemming
    • P - Stemming
    • U - Lemmatization
    • P - Lemmatization
    • U - Chunks
    • P - Chunks
    • U - Bag Of Words
    • P - Bag Of Words
    • U - Category Predictor
    • P - Category Predictor
    • U - Gender Identifier
    • P - Gender Identifier
    • U - Sentiment Analyzer
    • P - Sentiment Analyzer
    • U - Topic Modeling
    • P - Topic Modeling
    • Summary

    Feature Engineering

    • Using Google Colab
    • Introduction
    • One Hot Encoding
    • Count Vectorizer
    • N-grams
    • Hash Vectorizing
    • Word Embedding
    • FastText

    Dealing with corpus and WordNet

    • Introduction
    • In-built corpora
    • External Corpora
    • Corpuses & Frequency Distribution
    • Frequency Distribution
    • WordNet
    • Wordnet with Hyponyms and Hypernyms
    • The Average according to WordNet

    Create your Vocabulary for any NLP Model

    • Putting the previous knowledge together
    • Introduction and Challenges
    • 1 - Building your Vocabulary
    • 2 - Building your Vocabulary
    • 3 - Building your Vocabulary
    • 4 - Building your Vocabulary
    • 5 - Building your Vocabulary
    • Dot Product
    • Similarity using Dot Product
    • Reducing Dimensions of your Vocabulary using token improvement
    • Reducing Dimensions of your Vocabulary using n-grams
    • Reducing Dimensions of your Vocabulary using normalizing
    • Reducing Dimensions of your Vocabulary using case normalization
    • When to use stemming and lemmatization?
    • Sentiment Analysis Overview
    • Two approaches for sentiment analysis
    • Sentiment Analysis using rule-based
    • Sentiment Analysis using machine learning - 1
    • Sentiment Analysis using machine learning - 2
    • Summary

    Word2Vec in Detail and what is going on under the hood

    • Introduction
    • Bag of words in detail
    • Vectorizing
    • Vectorizing and Cosine Similarity
    • Topic modeling in Detail
    • Make your Vectors will more reflect the Meaning, or Topic, of the Document
    • Sklearn in a short way
    • Summary

    Find and Represent the Meaning or Topic of Natural Language Text

    • Note!
    • Keyword Search VS Semantic Search
    • Problems in TI-IDF leads to Semantic Search
    • Transform TF-IDF Vectors to Topic Vectors under the hood

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