Unit 8 Supervised classification

Activity 1 Machine learning fundamentals for NLP

Read about supervised learning principles applied to text data.

Supervised classification uses labeled training data to learn patterns that predict categories for new text. This forms the foundation of many NLP applications including sentiment analysis, spam detection, topic classification, and language identification.

Key concepts include:

• Training vs. Testing: Using labeled data to learn, then evaluating on unseen data
• Feature representation: Converting text to numerical vectors (bag-of-words, TF-IDF, embeddings)
• Classification algorithms: Naive Bayes, SVM, Logistic Regression, Neural Networks
• Cross-validation: Robust evaluation using multiple train/test splits
• Hyperparameter tuning: Optimizing algorithm settings for best performance
• Overfitting prevention: Regularization and validation strategies

Activity 2 Classification algorithms overview

Watch this comparison of major supervised learning algorithms.

This video (8 minutes) covers Naive Bayes, Support Vector Machines, Logistic Regression, and Decision Trees, explaining their strengths, weaknesses, and best use cases for text classification.

Activity 3 Algorithm comparison lab

Compare different classification algorithms on the same dataset.

Use the interactive tool to train and compare multiple algorithms on sample text data. Observe how different algorithms perform with various feature representations.

Activity 4 Feature engineering deep dive

Watch advanced feature engineering techniques for text classification.

This video (13 minutes) covers n-grams, character features, syntactic features, semantic embeddings, and feature selection methods to improve classification performance.

Activity 5 Feature engineering workshop

Experiment with different feature types and see their impact on performance.

Build custom feature sets and observe how they affect classification accuracy. Learn which features work best for different types of text classification tasks.

Activity 6 Model evaluation and selection

Learn comprehensive model evaluation techniques and selection criteria.

Master advanced evaluation techniques including cross-validation, learning curves, and statistical significance testing to choose the best models for production.

Activity 7 Hyperparameter optimization

Optimize model hyperparameters for maximum performance.

Learn systematic approaches to hyperparameter tuning using grid search, random search, and Bayesian optimization to achieve optimal model performance.