Learn to prepare data for your next machine learning project

Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

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What will you learn

  • Difference between NLP, NLU and NLG

  • Importing NLTK library for NLP

  • Sentiment Analysis, Text Classification, Topic Modeling ,andText Summarization

  • Understanding Tokenization and Bag of Words

  • What do Ngram means and its significance

  • Difference between Lemmatization and Stemming

  • Part of Speech(POS) tagging

  • What are Stopwords and its use in the context of NLP

  • What are TFIDF vector and its significance

  • Binary Text classification and Text classification

  • Applying NLP pre-processing for training model

  • Applying LinearSVC for binary classification

  • Applying OneVsRestClassifier for Multi-Label Classification

  • Applying MultiLabelBinarizer for Multi-Label Classification

  • Understanding the evaluation metrics used for NLP(Precision, F1-score, Recall)

  • Evaluating the performance of the model and scope of improvement

Project Description

Text cleaning and processing is an important task in every machine learning project where the task is to make sense of textual data. How to construct features from Text Data and further to it, create synthetic features are again critical tasks. On top of it how to apply machine learning models to create classifiers are also difficult. 
 

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Curriculum For This Mini Project

 
  Overview of NLP
06m
  Steps in NLP
02m
  Tokenization
01m
  Ngram Extraction
02m
  Creating Tokens
03m
  Installing NLTK Library
03m
  NLTK - word_tokenize
02m
  NLTK - TweetTokenizer
03m
  Stemming
03m
  Lemmatisation
05m
  Major Tasks for using NLP
00m
  Reuters Data Set
15m
  Stop words
04m
  Extract Ngrams
03m
  Steps in Text Classification
05m
  Recap
10m
  Import Libraries for Text Classification
00m
  Binary Text Classification
13m
  Problems with Text Classification
02m
  Represent, Train, Predict
04m
  Multiclass Classification Problem
04m
  Multi Label Problem
08m
  Evaluating Classifiers
06m
  Classifying Larger Corpus
11m
  Sparsity
04m
  Sentiment Analysis
15m
  Conclusion
00m