Binary Classification_ Simple Explanation of Logistic Regression

🎓 Lecture 5: Logistic Regression – Binary Classification | Machine Intelligence I In this lecture, Dr. Sahar Qaadan from the German Jordanian University (GJU) explains how to move from linear regression to logistic regression to solve binary classification problems. Through clear visuals and mathematical intuition, you’ll learn how models estimate probabilities, make confident decisions, and learn through optimization. 🔹 Key Learning Goals: 1️⃣ Understand the transition from Linear to Logistic Regression 2️⃣ Explore how the sigmoid function maps outputs to probabilities 3️⃣ Learn the concept of Maximum Likelihood Estimation (MLE) 4️⃣ Derive the Log-Likelihood and Binary Cross-Entropy Loss 5️⃣ See how the Gradient Descent optimizer tunes model parameters 6️⃣ Visualize the decision boundary using the Iris dataset 💻 Tools: Python | scikit-learn | NumPy | Matplotlib 📘 Course: Machine Intelligence I 👩‍🏫 Instructor: Dr. Sahar Qaadan, Vice Dean for Scientific Research, GJU #MachineLearning #AI #LogisticRegression #BinaryClassification #GradientDescent #PythonML #DataScience #SupervisedLearning #GermanJordanianUniversity #GJU #DrSaharQaadan #MachineIntelligence