---
metadata:
  - name: generator
    content: Diplodoc Platform v5.50.3
alternate:
  - https://catboost.ai/docs/en/concepts/tutorials.md
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# Tutorials

CatBoost is well covered with educational materials for both novice and advanced machine learners and data scientists.



#### Video tutorial

This tutorial gives a short introduction to CatBoost and showcases its' functionality in Jupyter Notebook.

#### Video

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@[youtube](../_includes/work_src/reusage-common-phrases/usdEWSDisS0)

Note that the links to the materials at the start of the presentation no longer work, you can find the Jupyter notebook [here](https://github.com/catboost/tutorials/blob/master/events/2019_pydata_london/pydata_london_2019.ipynb) instead.
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#### Getting started tutorials

- [CatBoost tutorial](https://github.com/catboost/tutorials/blob/master/python_tutorial.ipynb)
- [Solving classification problems with CatBoost](https://github.com/catboost/tutorials/blob/master/classification/classification_tutorial.ipynb)

These Python tutorials show how to start working with CatBoost.

Perform the following steps to use them:
1. Download the tutorials using one of the following methods:

    - Click the **Download** button on the github page
    - Clone the whole repository using the following command:
    ```bash
    git clone https://github.com/catboost/tutorials
    ```

1. Run Jupyter Notebook in the directory with the required `ipynb` file.

#### CatBoost on GPU

[This tutorial](https://github.com/catboost/tutorials/blob/master/tools/google_colaboratory_cpu_vs_gpu_tutorial.ipynb) shows how to run CatBoost on GPU with Google Colaboratory.

#### Tutorials in the CatBoost repository

The CatBoost repository contains [several tutorials](https://github.com/catboost/tutorials/) on various topics, including but no limited to:

- how to apply the model
- how to use custom losses
- how to train a ranking model
- how to perform hyperparameter search

#### Courses

Check out a free part of the [Introduction to Competitive Data Science](https://stepik.org/a/108888) course. The assignment helps to explore all basic functions and implementation features of the CatBoost [Python package](https://catboost.ai/docs/en/concepts/python-quickstart.md) and understand how to win a Data Science Competition. (in Russian)


#### Applying CatBoost models in ClickHouse

The ClickHouse documentation contains a [tutorial](https://github.com/ClickHouse/clickhouse-presentations/blob/master/tutorials/catboost_with_clickhouse_en.md) on applying a CatBoost model in ClickHouse.