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Mentions
Packaged an ML model as an API in Module 5 of #mlzoomcamp. ✅ Checked the pinned model artifact ✅ Served predictions with FastAPI ✅ Built and tested the Docker image My repo: ACADEMIC-AND-PERSONAL-PROJECTS/Homework-5-Deploying-Machine-Learning-Models
Packaged an ML model as an API in Module 5 of #mlzoomcamp. ✅ Checked the pinned model artifact ✅ Served predictions with FastAPI ✅ Built and tested the Docker image My repo: ACADEMIC-AND-PERSONAL-PROJECTS/Homework-5-Deploying-Machine-Learning-Models
Evaluated a classifier in Module 4 of #mlzoomcamp. ✅ Compared ROC AUC and threshold-based metrics ✅ Found a useful precision/recall trade-off ✅ Used 5-fold cross-validation for tuning My repo: ACADEMIC-AND-PERSONAL-PROJECTS/Homework-4-Evaluation
Evaluated a classifier in Module 4 of #mlzoomcamp. ✅ Compared ROC AUC and threshold-based metrics ✅ Found a useful precision/recall trade-off ✅ Used 5-fold cross-validation for tuning My repo: ACADEMIC-AND-PERSONAL-PROJECTS/Homework-4-Evaluation
Module 3 of #mlzoomcamp — Classification Built a lead scoring model with logistic regression: 🔹 One-hot encoding 🔹 Feature elimination 🔹 Validation Key takeaway: the best model isn't just about adding features, it's about finding the right ACADEMIC-AND-PERSONAL-PROJECTS/Homework-3-Machine-Learning-for-Classification @Al_Grigor