Data analyzing and Machine Learning Hands-on with KNIME

Hands-on crash course guiding through codeless, user-friendly, free data science software KNIME Analytics Platform

Created by: Barbora Stetinova, MBA
ElderberryData

What Will I Learn?

• Machine Learning in codeless KNIME Analytics Platform from A to Z – Classification and Regression
• Machine Learning models - Regression (simple linear, multilinear, polynomial, decision tree, random forest)
• Machine Learning models - Classification (decision tree, random forest, naive bayes, SVM, gradient booster)
• Data preparation for the machine learning predictive model with KNIME nodes
• Machine Learning model´s performance evaluation (confusion matrix, accuracy ratio, R squared)
• Collecting different data sources at one place
• Exploring data to understand its trend, relations etc.
• Using and working with Metanodes and Components
• Data normalization
• Outliers detection
• Understand KNIME environment, work with the workflow files and KNIME nodes
• Transform data by using basic KNIME nodes
• Visualize data by using charts, plots and statistics KNIME nodes (line plot, scatter plot, correlation matrix, box plot, histogram)
• Understand the basic theory and its importance of the AI, Big Data, Data Science and Machine Learning including several techniques
• Install and be able to work with the KNIME Analytics Platform environment
• Find help and advice when working with KNIME

Requirements

• Access to computer or laptop with Windows (32bit or 64 bit), Linux (64bit) or Mac (64bit) and with permission to download software (if not, ask your administrator)
• No prior knowledge required
• Basic data analyzing experience in different programs, like MS Excel or SQL or Python etc. is added advantage

Target audience

• anyone searching user-friendly, easily understandable, codeless 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 technologies in the data science field
• people willing to learn and use new modern tools for data analyzing and machine learning