KNIME Introduction to Machine Learning Algorithms

This course introduces you to the most common machine learning algorithms used in data science applications.

In the course, we will explore various 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 explore the latest advances in deep learning. In addition, we will look at 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 classical data preparation techniques such as normalization or dimensionality reduction.

• Introduction and Decision Tree Algorithm
• Regression Models, Ensemble Models, and Logistic Regression
• Neural Networks and Recommendation Engines
• Clustering and Data Preparation
• Wrap Up 

Please note: This course consists of four 75-minute online sessions conducted by a KNIME data scientist. Each session includes an exercise that you can complete at home. The course ends with a 15 to 30-minute final session.

This course is aimed at current and aspiring data scientists who want to learn more about machine learning algorithms commonly used in data science projects.

This course does not provide a detailed introduction to the KNIME Analytics Platform. You should be familiar with the KNIME Analytics Platform. We assume that you have already created KNIME workflows and are familiar with data processing concepts and techniques. We recommend attending this course after you have acquired KNIME L1 and L2 knowledge or an equivalent qualification.

You should already have the latest version of the KNIME Analytics Platform installed on your laptop, which you can download here: knime.com/downloads.

In this course, you will learn how to apply basic machine learning algorithms with KNIME. You will develop models for classification and regression tasks and evaluate their performance.