Women in AI Workshop @ EuroPython

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.

Agenda:

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