Introduction to Machine Learning with KNIME

Introduction

• Open-source machine learning with KNIME
• Who is this course for?

1. How Does KNIME Complement Your Existing Analytics Toolkit?

• Why use an Analytics Workbench?
• Using CRISP-DM to evaluate tools
• Why choose KNIME?

2. Getting Comfortable with KNIME

• The KNIME interface
• Find case studies on the Examples Server
• The KNIME Hub
• Add thousands of nodes with Extensions
• Search and Help

3. Accessing Data

• Accessing data
• File reader node
• Database access with KNIME

4. Data Understanding

• Describe data and verify data quality
• Explore data: Scatterplot
• Explore data: Boxplot

5. Data Integration and Merging

• Merging with the Joiner node
• Aggregating with the GroupBy node
• Creating new variables with Construct
• Select data with Column Filter
• Balancing data with Row Sampling node
• Clean data with the Missing Value node
• Format with Cell Splitter

6. Modeling

• KNIME modeling options
• Regression example
• Decision tree
• Decision tree: Scoring new data
• Components in KNIME: AutoML and XAI

7. A World of Possibilities

• PMML
• R and GGPLOT2
• Python options in KNIME
• Certification in KNIME

Conclusion

• Next steps