• SupportUkraineNow.org — "Real ways you can help Ukraine"

Before we dive in, here are some notable projects and initiatives that might interest you as well.

• 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"

• 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-earth
• daviddao/code-against-climate-change
• protontypes/open-sustainable-technology

Hi there! You might find this resource helpful if:

• You know Python or you're learning it 🐍
• 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.

• 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)

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"

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.

You just classified some hand-written digits using scikit-learn. Neat huh?

Let's learn a bit more about Machine Learning, and a couple of common ideas and concerns. Read "A Visual Introduction to Machine Learning, Part 1" by Stephanie Yee and Tony Chu.

It won't take long. It's a beautiful introduction ... Try not to drool too much!

OK. Let's dive deeper.

Read "A Few Useful Things to Know about Machine Learning" by Prof. Pedro Domingos. It's densely packed with valuable information, but not opaque. (Don't worry if you don't understand it all yet.) Take some time with this one.

• What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?
• Another handy term: "Data Engineering."
  • "MLOps" overlaps with Data Eng, and there's an introductory MLOps section later in this guide.

Next, code along with one or more of these notebooks.

• Series of notebooks:
  • 2022: rasbt/machine-learning-book — notebooks from Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili
• Dr. Randal Olson's Example Machine Learning notebook: "let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We've been tasked by our head of data science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone."
  • Launch in Binder, no installation steps required
• Various topical notebooks:
  • trekhleb/machine-learning-experiments
  • trekhleb/homemade-machine-learning

Find more great Jupyter Notebooks when you're ready:

• Jupyter's official Gallery of Interesting Jupyter Notebooks: Statistics, Machine Learning and Data Science (permalink)

Pick one of the courses below and start on your way.

Prof. Andrew Ng's Machine Learning is a popular and esteemed free online course. I've seen it recommended often. And emphatically.

It's recommended to grab a textbook to use as an in-depth reference. The two I saw recommended most often were Understanding Machine Learning and Elements of Statistical Learning. You only need to use one of the two options as your main reference; here's some context/comparison to help you pick which one is right for you.

You might like to have a pet project to play with, on the side. When you are ready for that, you could explore one of these: Awesome Public Datasets, paperswithcode.com/datasets, datasetlist.com, KKulma/climate-change-data

• Study tips for Prof. Andrew Ng's course, by Ray Li
• If you're wondering, Is it still a relevant course? or trying to figure out if it fits for you personally, check out these reviews:
  • Review: Andrew Ng's Machine Learning Course
  • The user reviews on Coursera

It's hard to make time available every week. So, you can try to study more effectively within the time you have available. Here are some ways to do that:

• "Learning How to Learn" by Barbara Oakley by Barbara Oakley, a free video course on Coursera.
• Prefer book/audiobook? These are great options:
  • Barbara Oakley's book A Mind for Numbers: How to Excel at Math and Science (reviews) — "We all have what it takes to excel in areas that don't seem to come naturally to us at first"
  • Make It Stick: the Science of Successful Learning (reviews)

I am not a machine learning expert. I'm just a software developer and these resources/tips were useful to me as I learned some ML on the side.

• Data science courses as Jupyter Notebooks:
  • Practical Data Science
  • Python Data Science Handbook, as Jupyter Notebooks
• microsoft/Data-Science-For-Beginners — added in 2021 — "10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'."
• See also microsoft/ML-For-Beginners

Start with the support forums and chats related to the course(s) you're taking.

Check out datascience.stackexchange.com and stats.stackexchange.com – such as the tag, machine-learning. There are some subreddits, like /r/LearningMachineLearning and /r/MachineLearning.

Don't forget about meetups. Also look for chat invitations on project pages and so on.

• /r/LearnMachineLearning
• /r/MachineLearning
• /r/DataIsBeautiful
• /r/DataScience
• Cross-Validated: stats.stackexchange.com
• ossu/data-science has a Discord server and newsletter

These debugging tools can be used inside (or outside) a Jupyter notebook:

• birdseye, snoop
• pandas-log

There are many more tools than that, but those might get you started, or might be especially useful while you're learning. Beyond learning, troubleshooting is more than just logs or debuggers, of course... there's also some MLOps links, later in this guide.

"Machine learning systems automatically learn programs from data." Pedro Domingos, in "A Few Useful Things to Know about Machine Learning." The programs you generate will require maintenance. Like any way of creating programs faster, you can rack up technical debt.

Here is the abstract of Machine Learning: The High-Interest Credit Card of Technical Debt:

Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.

If you're reading this guide, you should read that paper. You can also listen to a podcast episode interviewing one of the authors of this paper.

• Awesome Production Machine Learning, "a curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning." It includes a section about privacy-preserving ML, by the way!
• "Rules of Machine Learning: Best Practices for [Reliable] ML Engineering," by Martin Zinkevich, regarding ML engineering practices.
• The High Cost of Maintaining Machine Learning Systems
• Overfitting vs. Underfitting: A Conceptual Explanation
• 11 Clever Methods of Overfitting and How to Avoid Them
• "So, you want to build an ethical algorithm?" An interactive tool to prompt discussions (source)

That's not a comprehensive list, of course! They are just some gateways and starting-points. Know some other resources? Please share them, pull requests are welcome!

OpenReview.net "aims to promote openness in scientific communication, particularly the peer review process."

If you are learning about MLOps but find it overwhelming, these resources might help you get your bearings:

• MLOps Stack Template by Henrik Skogström
• Lessons on ML Platforms from Netflix, DoorDash, Spotify, and more by Ernest Chan in Towards Data Science
• MLOps Stack Canvas at ml-ops.org

Recommended awesomelists to save/star/watch:

• EthicalML/awesome-artificial-intelligence-guidelines
• EthicalML/awesome-production-machine-learning
• visenger/awesome-ml-model-governance
• visenger/awesome-MLOps
• eugeneyan/applied-ml

• 🐣 Replicate "makes it easy to share a running machine learning model"
  • Easily try out deep learning models from your browser
  • The demos link to papers/code on GitHub, if you want to dig in and see how something works
  • The models run in containers built by cog, "containers for machine learning."
    • It's an open-source tool for putting models into reproducible Docker containers.
    • You can put models in containers with just Python and YAML.
  • There's an API for Replicate to run predictions for you

Take note: some experts warn us not to get too far ahead of ourselves, and encourage learning ML fundamentals before moving onto deep learning. That's paraphrasing from some of the linked coursework in this guide — for example, Prof. Andrew Ng encourages building foundations in ML before studying DL. Perhaps you're ready for that now, or perhaps you'd like to get started soon and learn some DL in parallel to your other ML learnings.

When you're ready to dive into Deep Learning, here are some helpful resources.

• Dive into Deep Learning - An interactive book about deep learning (view on GitHub)
  • Quickstart:
    • Run this book locally, using Jupyter Notebooks
    • Run this book in your browser, using Google Colab
  • "The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code."
  • "You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning."

Machine Learning can be powerful, but it is not magic.

Whenever you apply Machine Learning to solve a problem, you are going to be working in some specific problem domain. To get good results, you or your team will need "substantive expertise" / "domain knowledge." Learn what you can, for yourself... But you should also collaborate with experts. You'll have better results if you collaborate with subject-matter experts and domain experts.

I couldn't say it better:

Machine learning won’t figure out what problems to solve. If you aren’t aligned with a human need, you’re just going to build a very powerful system to address a very small—or perhaps nonexistent—problem.

That quote is from "The UX of AI" by Josh Lovejoy. In other words, You Are Not The User. Suggested reading: Martin Zinkevich's "Rules of ML Engineering", Rule #23: "You are not a typical end user"

What are some ways one can practice?

Here are some additional Data Science resources:

• Python Data Science Handbook, as Jupyter Notebooks
• r0f1/datascience — "A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks."

From the "Bayesian Machine Learning" overview on Metacademy:

... Bayesian ideas have had a big impact in machine learning in the past 20 years or so because of the flexibility they provide in building structured models of real world phenomena. Algorithmic advances and increasing computational resources have made it possible to fit rich, highly structured models which were previously considered intractable.

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Here are some other guides to learning Machine Learning.

• Machine Learning for Software Engineers, by Nam Vu. In their words, it's a "top-down and results-first approach designed for software engineers." Definitely bookmark and use it. It can answer many questions and connect you with great resources.
• ujjwalkarn/Machine-Learning-Tutorials
• josephmisiti/awesome-machine-learning
• Courses by cloud vendors. These are usually high quality content but steer you heavily to use vendor-specific tools/services. To avoid getting locked into vendor specifics, you can make sure you're learning from other resources as well.
  • microsoft/ML-For-Beginners, microsoft/Data-Science-For-Beginners
  • Machine Learning Crash Course from Google (more of their options)
  • Amazon AWS (more of their options)
• 2022: Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili

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