First there is the ML learning part of the official course of KNIME
Validate your knowledge and skills with the KNIME Certification Program. Complete the certification for your chosen learning path, and become a KNIME-certified data analyst, data engineer, or data scientist.
Then you could check out the KNIME press if you like more reading about things:
This book includes data science case studies from IoT, financial industry, customer intelligence, social media, cybersecurity, and more.
This entry could also be relevant for you. It will also point you to the (free) Udemy course which also has a part on machine learning:
Like @HansS said. The best thing to learn about KNIME is to start doing something. A good starting point could be to take Kaggle competition and try to do everything in KNIME (from scratch) - this is how I started using it more. Or try to bring one of your daily tasks from Excel to KNIME. You will encounter all kinds of challenges but with the powerful resources of KNIME, you will overcome them and learn a lot.
Having said that. There is a free Udemy video course when you like this kind of lear…
Then well I will point you to two entries about some automatic machine learning with the help of KNIME and R or Python an H2O.ai since I think this will become more relevant in the future and you would have at least benchmark against it if you are more of a end user and not a highly sophisticated ML developer:
H2O.ai Automl - a powerful auto-machine-learning framework wrapped with KNIME
It features various models like Random Forest or XGBoost along with Deep Learning. It has wrappers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water).
One major parameter to set is the running time the model has to test various models and do some hyperparam…
H2O.ai Automl - a powerful auto-machine-learning framework wrapped with KNIME
It features various models like Random Forest or XGBoost along with Deep Learning. It has wrappers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water).
One major parameter to set is the running time the model has to test various models and do some hyperparam…
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