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The ML4Proteins repository provides educational material covering machine learning for protein engineering.

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ML4Proteins

Welcome to ML4Proteins! Here you will learn about the concepts and methods of machine learning for protein engineering.

Getting Started

To get started with the course material, I highly recommend setting up a Conda environment. Conda is an open-source package management and environment management system that makes it easy to install, run, and update software packages and their dependencies. You can choose between Conda and Miniforge for your setup. Conda is more comprehensive, while Miniforge is a minimal installer for Conda specifically designed for conda-forge.

Installation Steps

Installing Conda or Miniforge:

Conda

Visit the Anaconda distribution page and download the installer for your operating system. Follow the installation instructions provided on the website.

Miniforge

Visit the Miniforge GitHub page and download the installer for your operating system. Follow the installation instructions provided in the repository.

Creating a New Environment

Open your terminal (or Anaconda Prompt if you're on Windows) and create a new Conda environment specifically for this course by running:

conda create --name ml4proteins python=3.8
Here, ml4proteins is the name of the environment, and python=3.8 specifies the Python version.

Activating the Environment

Activate the newly created environment with:

conda activate ml4proteins

Installing Required Packages

With your environment activated, install the necessary packages for the course. These may include libraries for machine learning, data manipulation, and specific tools for protein analysis. For example:

conda install numpy pandas scikit-learn matplotlib seaborn

Installing Jupyter Lab

conda install conda-forge::jupyterlab

PyTorch

Please, also install PyTorch using the following link and insturctions. Once you entered your system details the install command might look something like this:

conda install pytorch torchvision torchaudio cpuonly -c pytorch

Verifying the Installation

After installation, you can verify that everything is set up correctly by checking the installed package versions. For example:

python --version
pip list

Next Steps

Once your environment is set up and ready, you can proceed to the course content. Make sure to always activate your ml4proteins environment when working on the course materials.

Happy Learning!

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