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

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