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.
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.
This course consists of four, 75-minutes online sessions run by one of our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 15 to 30 minute wrap up session.
- Session 1: Introduction and Decision Tree Algorithm
- Session 2: Regression Models, Ensemble Models, and Logistic Regression
- Session 3: Neural Networks and Recommendation Engines
- Session 4: Clustering and Data Preparation
You should already know how to build workflows using KNIME Analytics Platform. This course doesn’t provide an introduction to KNIME Analytics Platform - it focuses on more advanced concepts of automating and building workflows.
Your own laptop, ideally pre-installed with the latest version of KNIME Analytics Platform, which you can download at knime.com/downloads.
Download the latest free, open source version of knime here: knime.com/downloads
You’ll receive a zoom link with your registration confirmation. Make sure you have a stable internet connection!
Sure! The sessions will be recorded and you’ll have access to each one for seven days from the time the session is over.
Absolutely - fire away!