Python for anomaly detection: Machine Learning Technics (Isolation Forest, One-Class SVM (OCSVM)
This video provides a comprehensive overview of statistical and machine learning techniques essential for data analysis and anomaly detection. It begins by introducing Matplotlib and Seaborn for data visualization, emphasizing their role in exploratory data analysis and creating customizable plots. The core then shifts to fundamental concepts in machine learning, distinguishing between Regression (predicting continuous values) and Classification (predicting discrete categories), while also defining statistical concepts like the mean, median, and mode for measuring central tendency. Finally, it delves into advanced models, detailing Simple Linear Regression for prediction, and exploring various outlier detection methods like the Z-Score and IQR, culminating in detailed explanations of Decision Trees, Bagging, Isolation Forest, and One-Class SVM (OCSVM), all of which are crucial tools for isolating anomalies in complex datasets.