Classification Models, Logistic Regression, 05A

🚀 Course: ML & AI for Civil Engineers 🔁 Module 05: Classification Models 🧠 Lecture A: Logistic Regression 👇 Link to the notebook: https://tinyurl.com/242rvu49 🚀 Logistic regression tutorial: learn how to convert engineering features into class probabilities and make better binary classification decisions. This lecture explains linear scores, sigmoid probability, binary class probability, binary cross-entropy loss, gradient descent updates, decision thresholds, threshold selection, and confusion matrix evaluation using Python, NumPy, pandas, and Matplotlib. Through bridge inspection and repair-risk examples, you’ll see how weights, bias, probabilities, and a 0.5 cutoff turn raw data into inspect, repair, monitor, or failure predictions. Watch to build logistic regression from scratch and evaluate model decisions clearly. ▶️➕🔔 Don't forget to like, subscribe, and hit the notification bell! ⚠️ Disclaimer: AI Voice used. #tutorials, #logisticregression, #linearscores, #datavisualization, #sigmoid, #logisticregression, #probability, #logisticregression, #binaryclassification, #sigmoid, #python, #sigmoid, #logisticregression, #logisticregression, #gradientdescent, #machinelearning, #logisticregression, #binarycrossentropy, #gradientdescent, #machinelearning, #thresholds, #classification, #thresholding, #classification, #datavisualization, #python, #confusionmatrix, #machinelearning,