Course information may be found here.
You can find more details about the course at my homel.
Feel free to contact me ([email protected]) if you have any questions or want to discuss any topic from the course 😊
All authorship is mentioned where possible.
The aim of the exercise is to get an overview of the basic capabilities of the Pandas, Matplotlib and Seaborn libraries and be able to setup a Python Virtual Enviroment (venv)
The aim of the exercise is to learn basic techniques for visualization creation and interpretation using Matplotlib and Seaborn libraries.
Goal of the excercise is to learn about more advanced vizualization techniques using Matplotlib and Seaborn libraries.
Goal of the excercise is to learn how to use K-means implementation in the Scikit-learn library to perform clustering and subsequent cluster analysis on a Titanic dataset.
We will learn how to use another clustering algorithm - Hierarchical (or Agglomerative) clustering. The base principles and important hyper-parameters will be explained.
The goal of this excercise is to complete the hands-on experience task with similar task description as the first project has.
Goal of the excercise is to code selected part of the Decision tree algorithm which is focused on the optimum split part using gini index.
After that the scikit-learn implementation of the Decision tree basic usage will be demonstrated.
Goal of the excercise is to learn how to use Scikit-learn library for a classification tasks and evaluate the performance of the proposed models.
Goal of this excercise is to complete the hands-on experience of the classification task.
Goal of the excercise is to learn how to use Scikit-learn library for a regression tasks employing various linear regression models and moreover evaluate the performance of the proposed models.
Goal of the excercise is to learn how to use basic deep learning models in Scikit-learn and Keras.
Goal of the excercise is to learn how to save trained models and use selected advanced libraries like Plotly or Optuna.
Credit goes to prof. Ing. Jan Platoš, Ph.D.
python -m venv venv
- Activate
venvin Windows
.\venv\Scripts\Activate.ps1
- Activate
venvin Linux
source venv/bin/activate
pip install jupyter "jupyterlab>=3" "ipywidgets>=7.6"
pip install pandas matplotlib requests seaborn scipy scikit-learn optuna tensorflow plotly==5.18.0
jupyter lab