Introduction#
Course presentation#
Welcome!
The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background.
This hands-on workshop will provide you with the basics of machine learning using Python.
Machine learning is the field devoted to methods and algorithms that ‘learn’ from data. It can be applied to a vast range of different domains, from linguistics to physics and from medical imaging to history.
This workshop covers the basics of machine learning in a practical and hands-on manner, so that upon completion, you will be able to train your first machine learning models and understand what next steps to take to improve them.
We start with data exploration and prepare the data so that it is suitable for machine learning. Then we learn how to train a model on the data using scikit-learn. We learn how to select the best model from the trained models and how to use different machine learning models (like linear regression, logistic regression, and decision tree models). Finally, we discuss some of the best practices when starting your own machine learning project.
Setup instructions#
Installing Python#
Python is a popular language for scientific computing, and a frequent choice for machine learning as well. To install Python, follow the Beginner’s Guide or head straight to the download page.
Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance so you are ready to go as soon as the workshop begins.
Installing the required packages{#packages}#
Pip is the package management system built into Python. Pip should be available in your system once you installed Python successfully.
Open a terminal (Mac/Linux) or Command Prompt (Windows) in a location that you will use for the workshop and run the following commands.
1. Create a virtual environment#
Create a virtual environment called ml_workshop:
On Linux/macOs#
python3 -m venv ml_workshop
On Windows#
py -m venv ml_workshop
2. Activate the newly created virtual environment#
On Linux/macOs#
source ml_workshop/bin/activate
On Windows#
ml_workshop\Scripts\activate
Remember that you need to activate your environment every time you restart your terminal!
3. Install the required packages:#
On Linux/macOs#
python3 -m pip install matplotlib jupyter seaborn scikit-learn pandas
On Windows#
py -m pip install matplotlib jupyter seaborn scikit-learn pandas
Starting Jupyter Lab#
Jupyter Lab is compatible with Firefox, Chrome, Safari and Chromium-based browse Note that Internet Explorer and Edge are not supported. See the Jupyter Lab documentation for an up-to-date list of supported browsers.
To start Jupyter Lab, open a terminal (Mac/Linux) or Command Prompt (Windows) and type the command:
jupyter lab
Download datasets#
Download and extract this datasets.zip file into the location that you will use for the workshop (make sure it is the same location as where you created the virtual environment).
Prerequisites#
The course aims to be accessible without a strong technical background. The requirements for this course are:
basic knowledge of Python programming : defining variables, writing functions, importing modules
some prior experience with the NumPy, pandas and Matplotlib libraries is recommended but not required.
For a quick introduction on these requirements, you can go through these course materials or use the following resources:
MOOC material#
The MOOC material is developed publicly under the CC-BY license.
You can cite the original material through the project’s Zenodo archive using the following DOI: 10.5281/zenodo.7220306.
It is possible to use the rocket icon at the top of each notebook page to interactively execute the code cells via the Binder service.
The videos are available as YouTube playlist at the Inria Learning Lab channel:
https://www.youtube.com/playlist?list=PL2okA_2qDJ-m44KooOI7x8tu85wr4ez4f