Lesson 5. Introduction to Data Science


Different machine learning algorithms are available in KNIME Analytics Platform. For example classic and modern algorithms, supervised and unsupervised algorithms, algorithms from the field of statistics or from the machine learning community, for numerical predictions or for classification, requiring data sorted in time, or just a random sample of data.

The Learner-Predictor Construct

For supervised algorithms, a training phase, a test phase, and optionally an optimization phase are implemented before transferring the model into production. This video shows how to cover these phases in KNIME Analytics Platform through a Learner-Predictor construct.


Example of Training and Testing a Machine Learning Model

A data science cycle covers more than just training the algorithm. Actually it’s the whole process from raw data to model evaluation and deployment. In the video below we summarize the most common steps required to produce a predictive model.


We conclude here this [L2-DW] KNIME Analytics Platform for Data Wranglers: Advanced course. Well done!

You can practice more with basic machine learning models in the [L1-DS] KNIME Analytics Platform for Data Scientists: Basics course.