Detailed Course Outline
Topic 1 – Analytics Workflow
- Define terms related to analytics and data science
- Describe the analytics workflow
- Describe common usage scenarios
- Navigate Splunk Machine Learning Toolkit
Topic 2 – Training and Testing Models
- Split data for tes7ng and training using the sample command
- Describe the fit and apply commands
- Use the score command to evaluate models
Topic 3 – Regression: Predict Numerical Values
- Differentiate predictions from estimates
- Identify prediction algorithms and assumptions
- Model numeric predictions in the MLTK and Splunk Enterprise
Topic 4 – Clean and Preprocess the Data
- Define preprocessing and describe its purpose
- Describe algorithms that preprocess data for use in models
- Use FieldSelector to choose relevant fields
- Use PCA and ICA to reduce dimensionality
- Normalize data with StandardScaler and RobustScaler
- Preprocess text using Imputer, NPR, TF-IDF, and HashingVectorizer
Topic 5 – Clustering
- Define Clustering
- Identify clustering methods, algorithms, and use cases
- Use Smart Clustering Assistant to cluster data
- Evaluate clusters using silhouette score
- Validate cluster coherence
- Describe clustering best practices
Topic 6 – Forecasting Fields
- Differentiate predictions from forecasts
- Use the Smart Forecasting Assistant
- Use the StateSpaceForecast algorithm
- Forecast multivariate data
- Account for periodicity in each time series
Topic 7 – Detect Anomalies
- Define anomaly detection and outliers
- Identify anomaly detection use cases
- Use Splunk Machine Learning Toolkit Smart Outlier Assistant
- Detect anomalies using the Density Function algorithm
- View results with the Distribution Plot visualization
Topic 8 – Classify: Predict Categorical Values
- Define key classification terms
- Identify when to use different classification algorithms
- Evaluate classifier tradeoffs
- Evaluate results of multiple algorithms