Low-code Machine Learning with KNIME Competency (Intermediate Level)

  • 24m
  • 24 questions
The Low-code Machine Learning with KNIME Competency (Intermediate Level) benchmark measures your knowledge of creating KNIME data workflows and managing regression models. You will be evaluated on your skills in building classification models, clustering models in KNIME, and performing time series analysis. A learner who scores high on this benchmark demonstrates that they have good competency in using KNIME to perform data analytics.

Topics covered

  • analyze fields using histograms
  • create and use workflow annotations
  • decompose time series signals
  • describe how association rules mining works
  • determine the ideal number of clusters for a K-means model
  • encode and split data for machine learning
  • extract data from date fields
  • install and use an XGBoost classifier
  • load data for market basket analysis
  • perform association rule learning for market basket analysis
  • perform hyperparameter tuning and view the results
  • perform k-means clustering
  • perform principal component analysis (PCA) and visualize clusters using principal components
  • perform time series forecasting using the autoregressive integrated moving average (ARIMA) model
  • process missing and duplicate values
  • set up and use bivariate bar charts
  • standardize data and process outliers
  • standardize data to improve model performance
  • use gradient boosting models for machine learning
  • use linear regression models for machine learning
  • view and remove seasonality in time series data
  • visualize aggregated data in scatter plots
  • visualize clusters using scatter and box plots
  • visualize data using moving averages