Initiatives
Before we dive in, here are some notable projects and initiatives that might interest you as well.
Related to machine learning
- AlgorithmWatch — newsletter — "a non-profit research and advocacy organization that is committed to watch, unpack and analyze automated decision-making (ADM) systems and their impact on society."
daviddao/awful-ai— "Awful AI is a curated list to track current scary usages of AI — hoping to raise awareness"humanetech-community/awesome-humane-tech— "Promoting solutions that improve wellbeing, freedom and society"
Code against climate change
ProjectDrawdown/solutions— Project Drawdown — "Project Drawdown entered the climate conversation with the publication of the 2017 book. With The Drawdown Review in 2020, the project continues its mission to inspire and communicate solutions." Python and Jupyter Notebooks.philsturgeon/awesome-earthdaviddao/code-against-climate-changeprotontypes/open-sustainable-technology
Dive into Machine Learning
Hi there! You might find this resource helpful if:
- You know Python or you're learning it :snake:
- You're new to Machine Learning
- You care about the ethics of ML
- 8 Responsible Machine Learning Principles
- Open Ethics Canvas
- You learn by doing
For some great alternatives, jump to the end or check out Nam Vu's guide, Machine Learning for Software Engineers.
Of course, there is no easy path to expertise. Also, I'm not an expert! I just want to connect you with some great resources from experts. Applications of ML are all around us. I think it's in the public interest for more people to learn more about ML, especially hands-on, because there are many different ways to learn.
Whatever motivates you to dive into machine learning, if you know a bit of Python, these days you can get hands-on with a machine learning "Hello World!" in minutes.
Let's get started
Tools you'll need
If you prefer local installation
- Python. Python 3 is the best option.
- Jupyter Notebook. (Formerly known as IPython Notebook.)
- Some scientific computing packages:
- numpy
- pandas
- scikit-learn
- matplotlib
You can install Python 3 and all of these packages in a few clicks with the Anaconda Python distribution. Anaconda is popular in Data Science and Machine Learning communities. (Use whichever tool works for you. If you're unsure or need more context about using conda/virtualenv/poetry/pipenv, here's a very helpful guide)
Cloud-based options
Some options you can use from your browser:
- Binder is Jupyter Notebook's official choice to try JupyterLab
- Deepnote allows for real-time collaboration
- Google Colab provides "free" GPUs
For other options, see:
- markusschanta/awesome-jupyter, "Hosted Notebook Solutions"
- ml-tooling/best-of-jupyter, "Notebook Environments"
Let's go!
Learn how to use Jupyter Notebook (5-10 minutes). (You can learn by screencast instead.)
Now, follow along with this brief exercise: An introduction to machine learning with scikit-learn. Do it in ipython or a Jupyter Notebook, coding along and executing the code in a notebook.