Machine Learning fundamentals using Python

In this 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.


No public sessions are currently scheduled. We will be pleased to set up an on-site course or to schedule an extra public session (in case of a sufficient number of candidates). Interested? Please contact ABIS.

Intended for

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


Good Python programming knowledge is a prerequisite (see Python fundamentals).

Main topics

  • 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 Skicit-learn

Training method

Classroom teaching, focused on practical examples and supported by in-depth exercises and individual practice.


3 days.

Course leader