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1
Introduction
2
Overview
3
Machine Learning
4
Fitting a Model
5
Models in Splunk
6
Local Outlier Factor
7
Category Prediction
8
Numeric Prediction
9
Robust Scaler
10
Forecasting
11
Scoring
Description:
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.

Cyber & Data - Introduction to Splunk and Machine Learning

Bill Buchanan OBE
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