Hands-on crash course guiding through user-friendly and effective open data science software Knime Analytics Platform
Created by Barbora Stetinova, MBA
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Description
The goal of this course is to gain knowledge how to use open source Knime Analytics Platform for data analysis and machine learning predictive models on real data sets.
The course was done in KNIME analytics platform version 3.x (there can be minor diferencies in few nodes in comparison with 4.x version)
We will create machine learning models within the standard machine learning process way, which consists from:
acquiring data by reading nodes into the KNIME software (the data frames are available in this course for download)
pre-processing and transforming data to get well prepared data frame for the prediction
visualizing data with KNIME visual nodes (we will create basic plots and charts to have clear picture about our data)
creating machine learning predictive models and evaluating them:
1. Decision Tree Classification
2. Simple linear Regression
3. Decision Tree Regression
4. Random Forest Regression
5. Random Forest Classification
6. Polynomial Regression (+ info about multi linear Regression - for Knime same nodes)
7. Naive Bayes
8. K nearest neighbors
9. Grandient booster Regression
10. Grandient booster Classification
models 3 - 10 were added in the end of 2019.
I will also explain the Knime Analytics Platform environment, guide you through the installation , and show you where to find help and hints.
Who this course is for:
anyone searching user-friendly, easily understandable and highly useful tool for data analyzing and machine learning tasks without necessity to have programming skills
people working with several data sources of different file types
people working with data - both small and big data
anyone excited in learning new things in the data science field
people willing to learn and use new modern tools for data analyzing and machine learning
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