Explore an in-depth video lecture on Splunk and machine learning fundamentals. Learn about anomaly detection techniques, predictive modeling for categorical and numeric data, clustering algorithms, feature extraction methods, and data preprocessing. Gain hands-on experience with Splunk's machine learning commands for tasks like outlier detection, classification, regression, dimensionality reduction, and data scaling. Discover how to implement various algorithms including LocalOutlierFactor, decision trees, random forests, gradient boosting, and more using Splunk's ML Toolkit.

Syllabus

Introduction
Overview
Machine Learning
Fitting a Model
Models in Splunk
Local Outlier Factor
Category Prediction
Numeric Prediction
Robust Scaler
Forecasting
Scoring