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    Hands On Natural Language Processing (NLP) using Python

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 499 3099

    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
    ₹ 499  ₹3,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction to the Course

    • What is NLP?
    • Getting the Course Resources
    • Getting the Course Resources - Text

    Getting the required softwares

    • Installing Anaconda Python
    • Installing Anaconda Python - Text
    • A tour of Spyder IDE
    • How to take this course?

    Python Crash Course

    • Variables and Operations in Python
    • Conditional Statements
    • Introduction to Loops
    • Loop Control Statements
    • Python Data Structures - Lists
    • Python Data Structures - Tuples
    • Python Data Structures - Dictionaries
    • Console and File I/O in Python
    • Introduction to Functions
    • Introduction to Classes and Objects
    • List Comprehension
    • Test Your Skills

    Regular Expressions

    • Introduction to Regular Expressions
    • Finding Patterns in Text Part 1
    • Finding Patterns in Text Part 2
    • Substituting Patterns in Text
    • Shorthand Character Classes
    • Character Ranges - Text
    • Preprocessing using Regex
    • Test Your Skills

    Numpy and Pandas

    • Introduction to Numpy
    • Introduction to Pandas

    NLP Core

    • Installing NLTK in Python
    • Tokenizing Words and Sentences
    • How tokenization works? - Text
    • Introduction to Stemming and Lemmatization
    • Stemming using NLTK
    • Lemmatization using NLTK
    • Stop word removal using NLTK
    • Parts Of Speech Tagging
    • POS Tag Meanings
    • Named Entity Recognition
    • Text Modelling using Bag of Words Model
    • Building the BOW Model Part 1
    • Building the BOW Model Part 2
    • Building the BOW Model Part 3
    • Building the BOW Model Part 4
    • Text Modelling using TF-IDF Model
    • Building the TF-IDF Model Part 1
    • Building the TF-IDF Model Part 2
    • Building the TF-IDF Model Part 3
    • Building the TF-IDF Model Part 4
    • Understanding the N-Gram Model
    • Building Character N-Gram Model
    • Building Word N-Gram Model
    • Understanding Latent Semantic Analysis
    • LSA in Python Part 1
    • LSA in Python Part 2
    • Word Synonyms and Antonyms using NLTK
    • Word Negation Tracking in Python Part 1
    • Word Negation Tracking in Python Part 2

    Project 1 - Text Classification

    • Getting the data for Text Classification
    • Getting the data for Text Classification - Text
    • Importing the dataset
    • Persisting the dataset
    • Preprocessing the data
    • Transforming data into BOW Model
    • Transform BOW model into TF-IDF Model
    • Creating training and test set
    • Understanding Logistic Regression
    • Training our classifier
    • Testing Model performance
    • Saving our Model
    • Importing and using our Model

    Project 2 - Twitter Sentiment Analysis

    • Setting up Twitter Application
    • Initializing Tokens
    • Client Authentication
    • Fetching real time tweets
    • Loading TF-IDF Model and Classifier
    • Preprocessing the tweets
    • Predicting sentiments of tweets
    • Plotting the results

    Project 3 - Text Summarization

    • Understanding Text Summarization
    • Fetching article data from the web
    • Parsing the data using Beautiful Soup
    • Preprocessing the data
    • Tokenizing Article into sentences
    • Building the histogram
    • Calculating the sentence scores
    • Getting the summary

    Word2Vec Analysis

    • Understanding Word Vectors
    • Importing the data
    • Preparing the data
    • Training the Word2Vec Model
    • Testing Model Performance
    • Improving the Model
    • Exploring Pre-trained Models

    Conclusion

    • Where you go from here?

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