Low-code ML with KNIME: Building Clustering Models

KNIME 4.7+    |    Intermediate
  • 10 videos | 1h 3m 55s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Clustering is an unsupervised learning technique that finds logical groupings or clusters in your data, for example, identifying what social network users have the same interests and background. In this course, explore how clustering models seek to find logical groupings in your data. Next, construct a KNIME workflow to load and explore data for a clustering model. Then, fill in missing values using different imputation techniques, identify highly correlated variables, and deal with outliers. Fit a k-means clustering model on your data, identify clusters, and use scatter plots to visualize the clusters in your data. Finally, perform dimensionality reduction using principal component analysis (PCA) and use the silhouette score to evaluate the number of clusters that gives you the best clustering for your data. Upon course completion, you will be able to fit and evaluate clustering models on your data and visualize clusters using 2-D and 3-D visualizations.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Identify clustering models
    Load and explore data in knime
    Process missing values and high-correlation attributes
    Standardize data and process outliers
  • Perform k-means clustering
    Visualize clusters using scatter and box plots
    Perform principal component analysis (pca) and visualize clusters using principal components
    Determine the ideal number of clusters for a k-means model
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 46s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 5m 12s
    Upon completion of this video, you will be able to identify clustering models. FREE ACCESS
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    3.  Reading the Classification Dataset
    8m 4s
    In this video, find out how to load and explore data in KNIME. FREE ACCESS
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    4.  Imputing Missing Values and Checking Correlations
    5m 1s
    Discover how to process missing values and high-correlation attributes. FREE ACCESS
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    5.  Standardizing Data and Removing Outliers
    8m 37s
    During this video, you will learn how to standardize data and process outliers. FREE ACCESS
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    6.  Performing K-means Clustering
    6m 57s
    Find out how to perform k-means clustering. FREE ACCESS
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    7.  Visualizing Cluster Details
    8m 57s
    During this video, discover how to visualize clusters using scatter and box plots. FREE ACCESS
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    8.  Applying PCA and Performing 3D Visualization
    8m 50s
    Learn how to perform principal component analysis (PCA) and visualize clusters using principal components. FREE ACCESS
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    9.  Finding the Optimal Number of Clusters
    8m 14s
    In this video, discover how to determine the ideal number of clusters for a K-means model. FREE ACCESS
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    10.  Course Summary
    2m 17s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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