No-code Machine Learning with RapidMiner Competency (Intermediate Level)

  • 27m
  • 27 questions
The No-code Machine Learning with RapidMiner Competency (Intermediate Level) benchmark measures your ability to use Turbo Prep for data preparation and Auto Model for model building. You will be evaluated on your ability to train regression and classification models, perform hyperparameter tuning, deploy models to a local machine, use and evaluate clustering models, and perform windowing, differencing, and moving average computations on time series data. A learner who scores high on this benchmark demonstrates that they have good competency in using RapidMiner to perform data analytics.

Topics covered

  • assign roles to attributes
  • compare two classification models
  • compare two different models
  • create and visualize moving averages
  • create association rules on transaction data
  • decompose time series data into trend, seasonal, and remainder components
  • deploy a model to the local machine
  • encode data and select attributes
  • extract attributes and visualize data
  • forecast time series data using autoregressive integrated moving average (ARIMA)
  • forecast time series data with function and seasonal components
  • outline what market basket analysis is used for
  • partition data for machine learning
  • perform data preparation and cleansing
  • perform hyperparameter tuning
  • perform hyperparameter tuning to identify the ideal number of clusters
  • perform k-means clustering
  • perform principal component analysis (PCA) and visualize clusters
  • perform windowing on data
  • prepare data for clustering
  • prepare data for market basket analysis
  • remove highly correlated attributes
  • use the Auto Model tool for automating machine learning
  • use the Turbo Prep tool for automating data preparation
  • use Turbo Prep and Auto Model to prepare and cluster data
  • visualize data using word clouds and bar charts
  • visualize time series differencing