Скачать или смотреть 3 Anomaly Detection Techniques Explained | Statistical vs Distance vs Density (DBSCAN)

#AnomalyDetection #DataScience #DBSCAN

Anomaly detection is an important concept in Data Science and Machine Learning used to identify unusual patterns or abnormal data points in a dataset. In this video, you will learn the three main anomaly detection techniques used in Applied Data Science.

The video explains Statistical-Based, Distance-Based, and Density-Based Anomaly Detection methods in a simple and exam-oriented way for engineering students.

You will also learn the DBSCAN algorithm in detail, including its parameters such as Epsilon (ε) and MinPts, and how it detects anomalies in low-density regions of data.

This lecture is especially useful for students studying Applied Data Science, Machine Learning, Data Analytics, and Artificial Intelligence.

📚 Topics Covered

What is Anomaly Detection
Statistical-Based Anomaly Detection
Distance-Based Anomaly Detection
Density-Based Anomaly Detection
DBSCAN Algorithm Explained
Core Points, Border Points, and Noise Points
Simple Examples for Understanding

🎓 Subject

Applied Data Science
Module – Anomaly Detection
This explanation is designed to help students understand the concept clearly and prepare for university exams, interviews, and data science fundamentals.

🔎 Keywords

Anomaly Detection, Anomaly Detection Techniques, Outlier Detection, DBSCAN Algorithm, Density Based Anomaly Detection, Distance Based Outlier Detection, Statistical Anomaly Detection, Data Science Tutorial, Machine Learning Basics, Applied Data Science.