Learn to prepare data for your next machine learning project

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