Course information may be found here.
You can find more details about the course at Homel from Radek Svoboda.
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.
Big thanks to Radek Svoboda for all the materials.
Materials that should help you with the basic concepts from the mathematics and statistics needed by this course.
Any PR is welcome, whether you find a typo or you have better explanation
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
OR
python3 -m venv venv
- Activate
venvin Windows
.\venv\Scripts\Activate.ps1
- Activate
venvin Linux/macOS
source venv/bin/activate
- Navigate to the root folder where the requirements.txt is located
pip install -r requirements.txt
jupyter lab
- Download Jupyter extension
- Follow the How to create a Python Virtual Enviroment
- Directly open the fml_xx.ipynb in the VSCode
- Enjoy the local experience
- You can use same shortcuts as with the JupyteLab or Google Colab
- Sometimes the jupyter kernel may freeze, you can use the VSCode command to reload window
- MacOS
shift + command + P-> Reload Window - Windows/Linux
shift + alt + P-> Reload Window
- MacOS
- You can use the
pip install library_namedirectly in the Jupyter notebook