Machine Learning and AI Foundations: Decision Trees with KNIME.
(Online Course)
Author
Contributors
Published
Carpenteria, CA linkedin.com, 2022.
Format
Online Course
Status
Description
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Language
English
Notes
General Note
6/22/202212:00:00AM
Participants/Performers
Presenter: Keith McCormick
Description
Expand your data science skills and establish a strong foundation in codeless machine learning.
Description
Suggested prerequisites General familiarity with supervised machine learning Understanding of terms such as target variable, input variable, algorithm, and train/test partition Decision trees are transparent, available in every platform, and foundational to more advanced techniques like Random Forests and XGBoost. And if you’re a data scientist looking to pivot to machine learning, there’s arguably no better topic to kick off your learning journey. In this course, learn the essentials of machine learning pertaining to predictive analytics and working with decision trees. Along the way, instructor Keith McCormick provides demonstrations using the KNIME Analytics Platform, so you can grasp how these concepts work in real-world scenarios.
System Details
Latest version of the following browsers: Chrome, Safari, Firefox, or Internet Explorer. Adobe Flash Player Plugin. JavaScript and cookies must be enabled. A broadband Internet connection.
Citations
APA Citation, 7th Edition (style guide)
McCormick, K. (2022). Machine Learning and AI Foundations: Decision Trees with KNIME . linkedin.com.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)McCormick, Keith. 2022. Machine Learning and AI Foundations: Decision Trees With KNIME. linkedin.com.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)McCormick, Keith. Machine Learning and AI Foundations: Decision Trees With KNIME linkedin.com, 2022.
MLA Citation, 9th Edition (style guide)McCormick, Keith. Machine Learning and AI Foundations: Decision Trees With KNIME linkedin.com, 2022.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.