Introduction to Machine Learning with KNIME

Description

In this course, you will :

• Demonstrates how KNIME supports all phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in a single platform.
• Get started quickly—in 15 minutes or less—or stay for in-depth training on merging and aggregation, modelling, and data scoring.
• Learn how to extend KNIME's capabilities and integrate R and Python.

Syllabus :

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
• Add thousands of nodes with Extensions
• Search and Help

 

3. Accessing Data

• Accessing data
• File reader node

 

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

 

7. A World of Possibilities

• PMML
• R and GGPLOT2
• Other options

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