Online Course: [L4-ML] Introduction to Machine Learning Algorithms - Online

Course focus
At this course, we 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.

Who is the course for?
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.

How is the course structured?
This is an instructor-led course consisting 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.

Course content

  • 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
2 Likes

Hello,
Looking at the Session 3 Exercises -

It would be great if we could spend even just a few minutes in class - or discuss here, the “Missing values handling and dimensionality reduction” meta node which is part of the Preprocessing Metanode. In particular I’d like to understand the use of the Low Variance Filter followed by Linear Correlation and Correlation filter and, after this metanode, the Normalizer.

(That said - it may already be covered in Session 4 in Outlier detection!)

Thanks!
Alec McLure