LOGISTIC REGRESSION Concept in Machine Learning | Module 11
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Logistic Regression is a foundational classification algorithm in Machine Learning, widely used across industries for making binary and multi-class predictions. In this video, we introduce Logistic Regression, how it differs from Linear Regression, and how it's used in real-world decision-making processes.
🎯 Whether you're a beginner or a data enthusiast, this video will simplify the core concepts and equip you to apply logistic regression models effectively.
Timestamp ⏱️
00:00 - Intro
00:20 - What is Logistics Regression?
06:15 - Equation of Logistic Regression
09:06 - Types of Logistics Regression
📌 What You'll Learn:
💡 What is Logistic Regression? – Understanding the algorithm and its mathematical intuition.
📊 Logistic vs Linear Regression – Key differences and when to use which.
📈 Sigmoid Function Explained – How probabilities are converted into predictions.
🧠 Evaluation Metrics – Accuracy, Precision, Recall, F1-Score, ROC-AUC.
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📖 Books to Read on Logistic Regression & Classification:
📚 An Introduction to Statistical Learning – Gareth James, Daniela Witten
📚 Logistic Regression Using R – Bruce, Bruce & Gedeck
📚 Hands-On Machine Learning with Scikit-Learn & TensorFlow – Aurélien Géron
🎬 Movies & Documentaries to Watch:
🎥 Moneyball – Making data-driven decisions in baseball.
🎥 The Great Hack – Predictive analytics in political campaigns.
🎥 Coded Bias – The ethics and impact of AI classification systems.
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