Machine Learning fundamentals using Python (EN/NL/FR)

In this 3 day ABIS course we will learn the different aspects of Machine Learning (ML). We will begin by looking into the different problems that we can solve with ML and when and why we must use ML.

This course will get you started with the fundamentals of ML. We will put this knowledge into practice by building our own machine learning models in Python with the scikit-learn library.

Remark: Course description in English; Dutch and French versions are available on the ABIS website. Courses are planned in Dutch, English, and French. Consult the ABIS website for alternate course formats.

Main Topics - Content: 

• Basic concepts of Machine Learning
  • Supervised learning
  • Unsupervised learning
  • Regression
  • Classification
  • Clustering
  • Dimensionality reduction
  • Training/testing/validation
  • Accuracy
  • Confusion matrix
  • Overfitting
  • Bias-variance tradeoff
  • Regularization
• The different models of Machine Learning
  • Linear regression
  • Logistic regression
  • K-means
  • Hierarchical clustering
  • Decision tree
  • Random forest
• Building Machine Learning models with Scikit-learn

Audience: Anyone that wants to use machine learning to solve real-life problems.

Background: Good Python programming knowledge is a prerequisite

Didactics: Classroom instruction with practical exercises.

Duration: 3 days.