NPTEL exam preparation| Machine Learning and Deep Learning fundamentals and applications| MCQs pass

Parametric & Non-Parametric Density Estimation Explained | NPTEL Final Exam Preparation | Machine Learning Fundamentals

Dear all,
Get exam-ready with this complete explanation of Parametric and Non-Parametric Density Estimation — a crucial topic from NPTEL’s Fundamentals and Applications of Machine Learning and Deep Learning course.

This video covers:
✅ Concept of density estimation in ML
✅ Parametric methods – Gaussian, Normal, and Exponential models
✅ Non-Parametric methods – Histogram, k-NN, and Parzen window techniques
✅ Comparison of parametric vs non-parametric approaches
✅ NPTEL previous year exam-oriented questions

📚 Ideal for:

NPTEL exam aspirants (CSE / AI / ML / Data Science)

GATE, UPSC, and Competitive Exam preparation

Students learning Pattern Recognition and Probability Theory

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