Just wrapped up Module 6 of #mlzoomcamp

Learned about
🔹 Decision Trees for regression
🔹 Random Forest
🔹 Feature importance
🔹 XGBoost parameter tuning

✨More trees isn't always better. There's a sweet spot for model complexity vs performance

➡️ Next up: Midterm project

@Al_Grigor #DataTalkClub

Just wrapped up Module 5 of #mlzoomcamp

Learned about:

🔹 Deploying ML models as web services
🔹 Serving predictions with flask
🔹 Dependency mgt with Pipenv
🔹 Dockerizing Python apps

✨ Takeaway: ML models are only useful when they’re live

➡️ Next: Deploy in AWS ebs

@Al_Grigor @DataTalksClub

Just wrapped up Module 4 of #mlzoomcamp

Learned about:

🔹 Accuracy vs precision & recall
🔹 Confusion matrices
🔹 ROC curves & AUC for model evaluation
🔹 Cross-validation

✨ Key takeaway: AUC shows how likely a positive case ranks above a negative one.

➡️ Next: Model Deployment!

@DatatalkClub

Just wrapped up Module 3 of #mlzoomcamp

Learned about

🔹 Classification vs Regression
🔹 Feature importance
🔹 One-hot encoding
🔹 Logistic regression for binary classification
🔹 Model interpretation

Up Next: Module 4: Evaluation Metrics

@DataTalksClub