Gradient ML Course 2025/26

This repository contains hands-on regular .py files as well as Jupyter Notebooks designed to help you learn and experiment with key AI and machine learning concepts through practical examples.

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Getting Started

I. Install the required tools

You will need Python and Jupyter Notebook (or JupyterLab). Python can be installed from: https://www.python.org/downloads/. Make sure you install pip as well (it should install itself along with python).

1. Create a venv (optional but recommended on windows, mandatory for linux and mac):

python -m venv .venv

or like this in some cases:

python3 -m venv .venv

This should create a folder named .venv

2. Activate the venv

windows version

./.venv/Scripts/activate.exe

linux/mac version

. ./.venv/bin/activate

After this something like (.venv) should appear on the left side of your terminal

3. Install the required packages (e.g. jupyter):

pip install jupyter

or in some cases:

pip3 install jupyter

You can check how to install or verify a specific python package on pypi, e.g.: https://pypi.org/project/jupyter/

This is also the way to solve ModuleNotFoundError for pretty much any module

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II. Running a Python file

You can run a .py file like this:

python file.py

or in some cases

python3 file.py

But most of the time, using whatever run function is integrated into your IDE (editor, like vscode or Pycharm) is much more comfortable

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III. Running a Notebook

You can start Jupyter locally by running:

jupyter notebook

This will open a local web interface where you can browse and run any notebook in this course.

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Recommended Setup: PyCharm

We strongly recommend using PyCharm’s built-in Jupyter Notebook, which provides:

• Better code navigation and debugging tools
• Integrated virtual environments
• Seamless execution of notebook cells directly within the IDE

Students have free access to JetBrains Premium tools, including PyCharm Professional. You can claim your free educational license here: https://www.jetbrains.com/community/education/#students

After activation, simply open a notebook file (.ipynb) in PyCharm — it will automatically switch to notebook mode.

Other Recommended Setup: Vscode

Vscode is free for everyone, a bit simpler, more versatile and more extensible, but requires a bit more work to setup It can be downloaded here: https://code.visualstudio.com/download

Vscode relies more on extensions e.g. to edit jupyter notebooks and python in general, you will need those:

https://marketplace.visualstudio.com/items?itemName=ms-python.debugpy https://marketplace.visualstudio.com/items?itemName=ms-python.python https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-python-envs https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff

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Structure

Each notebook or .py file in this course is designed to help you practice and explore topics discussed during the lectures. They contain various tasks, exercises, and code sections to complete, allowing you to apply theoretical knowledge in a hands-on way.

Typical notebook contents include:

• Lecture-related topics - concepts directly connected to what was covered in class
• Theory overview - a short summary of key ideas and background
• Code examples - working snippets that illustrate the discussed concepts
• Exercises to complete - missing code fragments, functions, or logic you need to fill in
• Practice tasks - small applied challenges or exploratory problems related to the lecture topics

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Learn by doing

The best way to learn AI (and programming in general) is to build, test, and break things. Have fun exploring!