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