External KNIME Courses
Courses run by trusted KNIME experts and supported with frequent guest lectures by KNIME employees, lecture materials and practical exercises, curriculum support, and more.Contact Us
Introduction to Machine Learning with KNIME (LinkedIn Learning)
Instructor: Keith McCormick
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.
Code Free Data Science (Coursera)
Instructor: Nathasha Balac
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.
Data Analyzing and Machine Learning Hands-on with KNIME (Udemy)
Instructor: Barbora Stetinova
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. 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
KNIME - A Crash Course for Beginners (Udemy)
Instructor: Dan We
This is a hands-on course, so I expect you to “data prep” along with me. After finishing our data prep we briefly (!) cover the visualization part where we visualize our prepared dataset in Tableau and in Power BI Desktop (yes we briefly cover both tools and it is up to you which you prefer!)
Finally, we also briefly cover the predictive analytics capabilities of KNIME and see how easy Machine Learning in KNIME can be (again a brief introduction and no coding required!)
This course is practical and consists of a case study where you can and should follow along to solve tasks.
Automated Wages Forecast (FINLITIX)
Instructor: Tyron Stewart
In this practical course, the KNIME Analytics Platform will be used to build an automated salary and wages forecast from start to finish for a fictitious consulting company. The course will cover the full process of blending, transforming and performing all forecast calculations on multiple source files, before outputting the desired salary and wages forecasts for the company for each month of the next 5 years.
Machine Learning: From Theory to Deployment (University of Applied Sciences and Arts Western Switzerland)
Lecturers: Dominique Genoud
At the University of Applied Sciences and Arts Western Switzerland, Bachelor students are using KNIME during their last year.
This class is for beginners in Machine Learning. It is divided into two parts: Theory and Practical Project.
In the first part, It goes through all the basics in order to build a strong knowledge. In addition, basics like CRISP-DM, available frameworks, and ML in Python are studied.
The second part is the application of the knowledge gained in the theory. The format is a group practical project and goes from the analysis to the deployment.
Data Science Algorithms and Tools (University of Reading)
Lecturer: Dr. Carmen Lam
Automated data collection and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories. In this context, automated data analysis and data modelling tools and algorithms (Data Mining) are becoming essential components to any information system. Application areas of these techniques include scientific computing, intelligent business, direct marketing, customer relationship management, market segmentation, store shelf management, data warehouse management, fraud detection in e-commerce and in credit card transactions, etc.
Aims: The study of fundamental techniques and tools for data manipulation and transformation, and for data mining algorithms classification, regression, clustering, association rule mining. In particular, one of the leading platforms for Data Science and Machine Learning, KNIME, will be introduced and adopted for practical activities.