Set up Environment¶
This guides will teach you how to set up a Machine Learning environment with conda and VSCode.
Conda¶
Conda is a package manager for Python. It is a free and open source software that is used to install, update, and remove packages. It is also used to create and manage virtual environments.
Installation¶
Go to Anaconda and download the installer for your operating system. Then follow the installation instructions.
After completion, open your terminal and run this command to verify installation:
conda --version
Create Virtual Environment¶
We create virtual environment because it allows us to isolate dependencies for different types of projects we want to work on. I suggest Python 3.11 for Machine Learning projects as most packages you will need are compatible with this version. Let us create an environment called ml:
conda create -n ml python=3.11
After creation, we need to activate the environment:
conda activate ml
Now you will see (ml) at the beginning of your terminal prompt, which indicates that the environment is activated. To deactivate the environment, run this command:
conda deactivate
Install Packages¶
We use pip to install packages in conda environment. This environment.yml contains packages that I have been using. To install it, run this command:
conda env update --file environment.yml
Make sure you are already in ml environment before running the command!
VSCode¶
In fact, you can choose what ever IDE you like, such as PyCharm. My preference is VSCode as it is free and simple. You can download it from here.
Install Extensions¶
We commonly use Jupyter notebook for Machine Learning projects. So we need to install the Jupyter extension for VSCode. You can install it from here.
Your First Jupyter Notebook¶
Create a file named test.ipynb and open it in VSCode. Create a new cell, set the language to Python, and add this command:
print("Hello World!")
Click Run All at the top left bar, and the editor will ask you to select an intepreter. Choose the ml environment we created earlier. The run should be successful.
Last¶
Welcome to the world of Data Science! In fact, I would say that I am pretty much a beginner at least when I am writing this post. I am excited to share my journey with you. Hopefully, you should see that each post is a step forward for myself. Let's start learning!
Remember, Data Science is not easy. It takes time and effort to learn (and also for your model to learn lol). It is a relatively new domain and the resources for learners may not be as systematic as other subjects. You may also stuck at some phases, just like how I kinda stuck at top 10% on Kaggle competitions. But always be optimistic and try your best! I promise you that this would be fun!