EuroSciPy 2024

Introduction to Machine Learning with scikit-learn and Pandas
2024-08-27 , Room 6

With Machine Learning becoming a topic of high interest in the scientific community, over the years, many different programming languages and environments have been used for Machine Learning research and system development. Python is known as easy to learn, yet powerful programming languages and has become a popular choice among professionals and amateurs. This tutorial will provide instructions on the usage of two popular Python libraries: Scikit-learn and Pandas, in Machine Learning modeling.


With Machine Learning becoming a topic of high interest in the scientific community, over the years, many different programming languages and environments have been used for Machine Learning research and system development. Python is known as easy to learn, yet powerful programming languages and has become a popular choice among professionals and amateurs. This tutorial will provide instructions on the usage of two popular Python libraries: Scikit-learn and Pandas, in Machine Learning modeling.
The tutorial includes:
- data preparation for ML modeling
- introduction of basic ML models
- implementation of a basic ML model


Abstract as a tweet:

Introduction to Machine Learning in Python with Scikit-learn and Pandas!

Category [High Performance Computing]:

Other

Category [Community, Education, and Outreach]:

Learning and Teaching Scientific Python

Category [Machine and Deep Learning]:

Supervised Learning

Category [Scientific Applications]:

Other

Category [Data Science and Visualization]:

Other

Expected audience expertise: Domain:

none

Expected audience expertise: Python:

some

See also: Github repository

I'm a Research Assistant in the Department of IT in Management, University of Szczecin, Szczecin, Poland. In my scientific work, I mainly focus on machine learning applications in business and programming teaching methods. I also teach classes at the university on topics including programming, machine learning, algorithms, data structures, etc.