Python Machine Learning

More and more organizations use machine learning to extract valuable insights and predictions from data. In this training, you will learn to apply these algorithms yourself using Python. You will work with widely used data science packages and gain a solid understanding of both the theory and practical application of machine learning.

We will cover the most important algorithms for classification and regression and show you how to build, evaluate, and optimize models. Through exercises in a personal online environment, you will apply the theory directly to realistic datasets. You will also explore techniques such as feature engineering and parameter tuning to improve your model’s performance.

You will learn

How to apply machine learning algorithms to real-world data
The possibilities of generative AI
How to build, train, and optimize predictive models
How to understand the strengths and limitations of various algorithms

Topics

Introduction to machine learning (supervised, unsupervised, and reinforcement learning)
Regression: predicting numerical values
Classification: dividing data into categories
Unsupervised learning and clustering, including k-means
Introduction to generative AI (GPT, DALL·E) and applications with Python
Model evaluation with ROC curve, AUC, confusion matrix
Improving models with feature engineering and hyperparameter tuning
Deploying machine learning models to production
Ensemble methods: stacking, bagging, and boosting

Who should attend?

This training is intended for data scientists, data analysts, BI specialists, and other professionals who have experience with programming in Python or a similar programming language and work with large, complex datasets. It is suitable for those looking to better predict trends using machine learning.

Prior knowledge

Basic skills in Python or a comparable programming language are required for this training.

Price per person

-- (excluding VAT)

Duration of this course

2 days

Course dates

22 - 23 may 2026
22 - 23 june 2026