Text Classification with Natural Language Processing

This advanced course path focuses on the end-to-end workflow for text classification in natural language processing. You will learn how to gather, clean, and prepare textual datasets for supervised classification tasks. The path covers practical feature engineering techniques such as tokenization, Bag-of-Words, TF-IDF, sparse feature handling, and dimensionality reduction. You will then apply modeling methods including Naive Bayes, support vector machines, decision trees, random forests, ensemble techniques, and deep learning with Python and TensorFlow. This path is designed for learners who already have experience with Python and machine learning and want to deepen their ability to build, evaluate, and improve NLP classification systems.

Syllabus

Prepare textual datasets for supervised classification tasks
Transform raw text into machine learning features using tokenization, Bag-of-Words, and TF-IDF
Apply dimensionality reduction and sparse feature handling techniques
Train text classification models using Naive Bayes, support vector machines, decision trees, and random forests
Use stratified cross-validation to evaluate models on imbalanced text datasets
Build advanced classifiers with ensemble methods and deep learning
Optimize text classification workflows for stronger model performance