This is the website for an older EuroPython. Looking for the latest EuroPython? Click here!
Skip to main content

Introduction to Machine Learning with Scikit-Learn

Description: Python is the most popular language for Machine Learning. Women in AI is delighted to bring a half-day workshop for introducing the intuition behind machine learning along with a series of hands-on sessions to implement machine learning models using Pandas and Scikit-Learn Python libraries. This workshop is designed to explain concepts in byte-sized contents and focus primarily on Python programming.

  • Who can join: Anyone with a EuroPython 2023 Tutorial Ticket or Combined Ticket or In-Person Conference Tickets. Slots are available for 30 participants. We appreciate if you could register your interest in the form below to help us plan!
  • When: 13:45–17:00, Monday 17 July
  • Where: Prague Congress Centre (PCC)), Room TBC
  • What do you need to bring? A laptop with internet access. We will be working on Google Colab on your web browser, hence, no prior set-up will be required.
  • Level: Beginner (Basic prior knowledge of Python is helpful but is not required for this workshop; No prior knowledge of Machine Learning is expected)
Be Part of the Women in AI Workshop
Register your interest now!

Who are we?

Women in AI (WAI) is a nonprofit do-tank working towards inclusive AI that benefits global society. We are a community-driven initiative bringing empowerment, knowledge, and active collaboration via education, research, events, and blogging. At Women in AI, we empower women and minorities to become AI & Data experts, innovators, and leaders. We encourage ethical applications and responsible use of artificial intelligence.


Hour One
    1. (1) Introduction to Machine Learning (Talk)
    • What is Machine Learning?
    • What is the ML-AI ecosystem?
    • Types of Machine Learning techniques
    • Real-world Applications of Machine Learning
    1. (2) The Philosophy Behind Machine Learning (Talk and Hands-on)
    • Training, Validation, and Loss
    • Concept of (Stochastic) Gradient Descent and Learning Rate
    • Concept of Generalization & Cross-Validation
    • Problems with Underfitting or Overfitting
    1. (3) Introduction to ScikitLearn as a Python Library for ML
Hour Two

(Immersive Hands-On Session)

    1. (4) ML Workflow Overview
    • Data Cleaning & Preparation
    • Data Splitting
    • Feature Engineering
    1. (5) Implementing a Regressor using Scikit Learn
    • Linear Regression
    • Evaluation Techniques for Regression
Hour Three

(Immersive Hands-on continued)

    1. (6) Implementing a Classifier using Scikit Learn
    • Data Cleaning & Preparation
    • Feature Engineering
    • Logistic Regression and Decision Trees
    • Evaluation Techniques for Classification
    1. (7) Conclusion