Linear & Logistic Regression in Machine Learning (2025 Guide) | Supervised Learning | Ch 4 – Pt1 скачать в хорошем качестве

Linear & Logistic Regression in Machine Learning (2025 Guide) | Supervised Learning | Ch 4 – Pt1

Welcome to Part II of Machine Learning Essentials – Supervised Learning from the AI Unlocked series! 🤖 In this video, we explore one of the most important topics in AI & Machine Learning — Regression. You’ll learn: 📘 What is Regression in Supervised Learning and why it matters. ⚙️ Types of Regression – Linear vs Logistic, with real-world examples. 📈 Linear Regression – concept, formula, evaluation metrics (MAE, MSE, R²), and assumptions. 📊 Logistic Regression – sigmoid function, probabilities, decision boundaries, and evaluation metrics (Accuracy, Precision, Recall, F1, ROC-AUC). 🧠 Regularization Techniques – L1 (Lasso) & L2 (Ridge) to prevent overfitting. 💡 Real-World Applications – sales forecasting, healthcare, finance, spam detection, cybersecurity, and more. Finally, we compare Linear vs Logistic Regression side-by-side to understand how they work together as the foundation of modern AI models. 🎓 Perfect for Beginners and Working Professionals who want to understand core AI concepts in a clear, practical way. 👉 Watch till the end to master how AI models learn to predict values and classify outcomes! Don’t forget to Like 👍 Share 🔁 and Subscribe 📲 to Practical AI Pro for more AI learning videos.