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CodeSignal

Feature Engineering for Text Classification

via CodeSignal

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Overview

Dive deeper into the transformation of raw text data into features that machine learning models can understand. Through a practical, hands-on approach, you'll learn everything from tokenization, generating Bag-of-Words and TF-IDF representations, to handling sparse features and applying Dimensionality Reduction techniques.

Syllabus

  • Unit 1: Tokenization: The Gateway to Text Classification
    • Filter Punctuation from Tokenized Review
    • Filtering Word Tokens from a Sentence
    • Completing Code for Data Loading and Tokenizing
    • Tokenizing and Filtering a Movie Review
    • Tokenizing First Review and Printing Tokens
  • Unit 2: Implementing Bag-of-Words Representation
    • Customizing Bag-of-Words Representation
    • Applying CountVectorizer on Sentences
    • Bag-of-Words Transformation on IMDB Reviews Dataset
    • Creating Bag-of-Words Representation Yourself
    • Turn Rich Text into Bag-of-Words Representation
  • Unit 3: Implementing TF-IDF for Feature Engineering in Text Classification
    • Change TF-IDF Vector for Different Sentence
    • Implementing TF-IDF Vectorizer on Provided Text
    • Understanding Sparse Matrix Components
    • Applying TF-IDF Vectorizer On Reviews Dataset
    • Implementing TF-IDF Vectorizer from Scratch
  • Unit 4: Efficient Text Data Representation with Sparse Matrices
    • Switching from CSC to CSR Representation
    • Creating a Coordinate Format Matrix with Duplicates
    • Performing Vectorized Operations on Sparse Matrices
    • Creating CSR Matrix from Larger Array
    • Performing Subtraction Operation on Sparse Matrix
  • Unit 5: Applying TruncatedSVD for Dimensionality Reduction in NLP
    • Change TruncatedSVD Components Number
    • Implement Dimensionality Reduction with TruncatedSVD
    • Applying TruncatedSVD on Bag-of-Words Matrix
    • Implement TruncatedSVD on Bag-of-Words Matrix
    • Implementing TruncatedSVD on IMDB Movie Reviews Dataset

Reviews

5.0 rating, based on 1 Class Central review

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  • StephD
    5
    Feature Engineering for Text Classification” is a must-take course for anyone looking to deepen their understanding of machine learning and natural language processing. It provides a comprehensive overview of the techniques required to transform raw text data into meaningful features for classification tasks. The course blends theory and practical applications seamlessly, equipping learners with the skills to preprocess and represent text data effectively. Clear explanations, hands-on examples, and real-world datasets make the content engaging and actionable. Whether you’re new to text classification or looking to refine your skills, this course is an invaluable resource for building robust, high-performing models.

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