Introduction to Machine Learning with KNIME
1h 42mIntermediate2019-06-05
Authors
Keith McCormick
Data Miner, Trainer, Speaker, Author
Course details
KNIME is an open-source workbench-style tool for predictive analytics and machine learning. It is highly compatible with numerous data science technologies, including R, Python, Scala, and Spark. With KNIME, you can produce solutions that are virtually self-documenting and ready for use. These reasons and more make KNIME one of the most popular and fastest-growing analytics platforms around. In this course, expert Keith McCormick shows how KNIME supports all the phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in one platform. Get up and running quickly—in 15 minutes or less—or stick around for the more in-depth training covering merging and aggregation, modeling, and data scoring. Plus, learn how to increase the power of KNIME with extensions and integrate R and Python.
Learning objectives
Why use a workbench
Why choose KNIME?
Adding KNIME nodes with extensions
Accessing data
Exploring data statistically and visually
Merging and aggregating data in KNIME
Modeling in KNIME
Scoring new data
Combining KNIME with R and Python
Learning objectives
Why use a workbench
Why choose KNIME?
Adding KNIME nodes with extensions
Accessing data
Exploring data statistically and visually
Merging and aggregating data in KNIME
Modeling in KNIME
Scoring new data
Combining KNIME with R and Python
Skills covered
KNIMEIntroduction toMachine LearningData AnalysisArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and Tools
Concepts
0. Introduction
- 01 - Open-source machine learning with KNIME
- 02 - Who is this course for
1. How Does KNIME Complement Your Existing Analytics Toolkit
- 03 - Why use an Analytics Workbench
- 04 - Using CRISP-DM to evaluate tools
- 05 - Why choose KNIME
2. Getting Comfortable with KNIME
- 06 - The KNIME interface
- 07 - Find case studies on the Examples Server
- 08 - Add thousands of nodes with Extensions
- 09 - Search and Help
3. Accessing Data
- 10 - Accessing data
- 11 - File reader node
4. Data Understanding
- 12 - Describe data and verify data quality
- 13 - Explore data - Scatterplot
- 14 - Explore data - Boxplot
5. Data Integration and Merging
- 15 - Merging with the Joiner node
- 16 - Aggregating with the GroupBy node
- 17 - Creating new variables with Construct
- 18 - Select data with Column Filter
- 19 - Balancing data with Row Sampling node
- 20 - Clean data with the Missing Value node
- 21 - Format with Cell Splitter
6. Modeling
- 22 - KNIME modeling options
- 23 - Regression example
- 24 - Decision tree
- 25 - Decision tree - Scoring new data
7. A World of Possibilities
- 26 - PMML
- 27 - R and GGPLOT2
- 28 - Other options
Conclusion
- 29 - Next steps