Text Classification with Natural Language Processing
Natural Language Processing
4 courses
110 practices
21 hours
This NLP learning path is tailored for mastering text classification, spanning from data preparation to model optimization. It includes practice in data collection, feature engineering, diverse modeling techniques, and advanced optimization strategies.
Collecting and Preparing Textual Data for Classification
5 lessons
25 practices
Learn how to collect and prepare specific textual datasets essential for your text classification project. You'll delve into the practices of gathering and cleaning text data, and explore advanced textual processing techniques.
Introduction to Modeling Techniques for Text Classification
6 lessons
Course 4
Advanced Modeling for Text Classification
6 lessons
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25 practices
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.
This course paves the way for your journey in NLP modeling. Delve deep into the world of text classification algorithms starting with Naive Bayes, Support Vector Machines, Decision Trees, and Random Forests. But that's not all!
You'll also get familiar with stratified cross-validation, a key tool for handling imbalanced classes in text data.
Explore advanced text classification techniques, including ensemble methods and deep learning, to enhance model performance using Python and TensorFlow.
