12 - End-to-End ML Pipeline: From Dataset to Production
Integrate everything: build a complete ML pipeline from A to Z. Data loading → preprocessing → feature engineering → model training → evaluation → dep
Mar 20, 20261 min read
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#data-science
Articles tagged with #data-science
11 - Transfer Learning and Fine-Tuning: Reusing Pre-trained Models
Accelerate ML: reuse pre-trained models with transfer learning. Learn when it makes sense, how fine-tuning works, layer freezing vs unfreezing, data a
Mar 20, 20261 min read10 - Recommendation Systems: Collaborative and Content-Based Filtering
Discover how recommendation systems work (Amazon, Netflix). Collaborative filtering (user-item matrix, similarity), content-based (feature similarity)
Mar 20, 20261 min read09 - Ensemble Methods: Bagging, Boosting, and Gradient Boosting
Combine multiple models for better results: bagging (low variance), boosting (low bias), gradient boosting (state-of-the-art). Discover Random Forest,
Mar 20, 20261 min read08 - Overfitting, Underfitting, and Regularization Techniques
Solve the fundamental ML problem: the bias-variance tradeoff. Understand overfitting (too complex), underfitting (too simple), and balancing technique
Mar 20, 20261 min read07 - Evaluation Metrics: Precision, Recall, F1-Score, and ROC-AUC
Learn to correctly evaluate ML models beyond accuracy. Confusion matrix, precision, recall, F1-score, ROC curve, AUC. When to use each metric, how to
Mar 20, 20261 min read