[L4-ML] Introduction to Machine Learning Algorithms

March 31, 2020 - Berlin

This course introduces you to the most commonly used Machine Learning algorithms used in Data Science applications. 

At the course we will explore different supervised algorithms for classification and numerical problems such as decision trees, logistic regression, and ensemble models. We will also look at recommendation engines and neural networks and investigate the latest advances in deep learning. In addition, we will examine unsupervised learning techniques, such as clustering with k-means, hierarchical clustering, and DBSCAN.

We will also discuss various evaluation metrics for trained models and a number of classic data preparation techniques, such as normalization or dimensionality reduction.

Each lesson includes a few hands-on exercises to understand better how the algorithm works and the importance of the various settings.

This course is designed for current and aspiring data scientists who would like to learn more about machine learning algorithms used commonly in data science projects.

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What level of KNIME experience is needed for this course?

You must be competent in using KNIME Analytics Platform. We strongly recommend you be at the level of an advanced KNIME user -  for example you’ve taken a basic and advanced KNIME Analytics Platform Course and/or use KNIME on a regular basis.

What do I need to bring?

Your own laptop, ideally pre-installed with the latest version of KNIME Analytics Platform, which you can download at

Where do I find the latest version of KNIME Analytics Platform?

Download the latest free, open source version of KNIME here:

What other resources will help me to get started with KNIME?