Hi there! This guide is for you:
I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself.
Note: There are several fields within "Data" and Machine Learning is just one. It's good to know the context: What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?
I suggest you get your feet wet ASAP. You'll boost your confidence.
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.
If you're using Python 2.7, don't worry. You don't have to migrate to Python 3 just for this guide. Also, if you're using pip/virtualenv instead of Anaconda, that's alright too! And re: installing packages, this is a helpful doc: conda vs. pip vs. virtualenv
Now, follow along with this brief exercise (10 minutes): An introduction to machine learning with scikit-learn. Do it in
ipython or IPython Notebook. It'll really boost your confidence.
You just classified some hand-written digits using scikit-learn. Neat huh?
I encourage you to look at the scikit-learn homepage and spend about 5 minutes looking over the names of the strategies (Classification, Regression, etc.), and their applications. Don't click through yet! Just get a glimpse of the vocabulary.
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. The author understands that there's a lot of "black art" and folk wisdom, and they invite you in.
Take your time with this one. Take notes. Don't worry if you don't understand it all yet.
The whole paper is packed with value, but I want to call out two points:
When you work on a real Machine Learning problem, you should focus your efforts on your domain knowledge and data before optimizing your choice of algorithms. Prefer to Do Simple Things until you have to increase complexity. You should not rush into neural networks because you think they're cool. To improve your model, get more data. Then use your knowledge of the problem to explore and process the data. You should only optimize the choice of algorithms after you have gathered enough data, and you've processed it well.
(Chart inspired by a slide from Alex Pinto's talk, "Secure Because Math: A Deep-Dive on ML-Based Monitoring".)
Before you take a break, grab some podcasts.
First, download an interview with Prof. Domingos on the Data Skeptic podcast (2018). Prof. Domingos wrote the paper we read earlier. You might also start reading his book, The Master Algorithm by Prof. Pedro Domingos, a clear and accessible overview of machine learning.
Next, subscribe to more machine learning and data science podcasts! These are great, low-effort resources that you can casually learn more from. To learn effectively, listen over time, with plenty of headspace. Do not speed up your podcasts!
Subscribe to Talking Machines.
I suggest this listening order:
OK! Take a break, come back refreshed.
Next, pick one or two of these IPython Notebooks and play along.
There are more places to find great IPython Notebooks:
Know another great notebook? Please submit a PR!
Now you should be hooked, and hungry to learn more. Pick one of the courses below and start on your way.
It's helpful if you decide on a pet project to play around with, as you go, so you have a way to apply your knowledge. You could use one of these Awesome Public Datasets. And remember, IPython Notebook is your friend.
Also, you should grab an in-depth textbook to use as a reference. The two best options are Understanding Machine Learning and Elements of Statistical Learning. You'll see these recommended as reference textbooks. 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 can download each book free as PDFs at those links - so grab them!
Here are some other free online courses I've seen recommended. (Machine Learning, Data Science, and related topics.)
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/machinelearning.
There are also many relevant discussions on Quora, for example: What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?
For help and community in meatspace, seek out meetups. Data Science Weekly's Big List of Data Science Resources may help you.
You'll want to get more familiar with Pandas.
Some good cheat sheets I've come across. (Please submit a Pull Request to add other useful cheat sheets.)
I'm not repeating the materials mentioned above, but here are some other Data Science resources:
... 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.
You can learn more by studying one of the following resources. Both resources use Python, PyMC, and Jupyter Notebooks.
"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 following this guide, you should read that paper. You can also listen to a podcast episode interviewing one of the authors of this paper.
A few more articles on the challenges running ML-powered systems in Production:
So you are dabbling with Machine Learning. You've got Hacking Skills. Maybe you've got some "knowledge" in Domingos' sense (some "Substantive Expertise" or "Domain Knowledge"). This diagram is modified slightly from Drew Conway's "Data Science Venn Diagram." It isn't a perfect fit for us, but it may get the point across:
Please don't sell yourself as a Machine Learning expert while you're still in the Danger Zone. Don't build bad products or publish junk science. (Also please don't be evil.) This guide can't tell you how you'll know you've "made it" into Machine Learning competence ... let alone expertise. It's hard to evaluate proficiency without schools or other institutions. This is a common problem for self-taught people.
You need practice. On Hacker News, user olympus commented to say you could use competitions to practice and evaluate yourself. Kaggle and ChaLearn are hubs for Machine Learning competitions. You can find some examples of code for popular Kaggle competitions here. For smaller exercises, try HackerRank.
You also need understanding. You should review what Kaggle competition winners say about their solutions, for example, the "No Free Hunch" blog. These might be over your head at first but once you're starting to understand and appreciate these, you know you're getting somewhere.
Competitions and challenges are just one way to practice. You shouldn't limit yourself, though - and you should also understand that Machine Learning isn't all about Kaggle competitions.
Here's a complementary way to practice: do practice studies.
And repeat. Re-phrasing this, it fits with the scientific method: formulate a question (or problem statement), create a hypothesis, gather data, analyze the data, and communicate results. (You should watch this video about the scientific method in data science, and/or read this article.)
How can you come up with interesting questions? Here's one way. Every Sunday, browse datasets and write down some questions. Also, sign up for Data is Plural, a newsletter of interesting datasets; look at these, datasets, and write down questions. Stay curious. When a question inspires you, start a study.
I think the best advice is to tell people to always present their methods clearly and to avoid over-interpreting their results. Part of being an expert is knowing that there's rarely a clear answer, especially when you're working with real data.
As you repeat this process, your practice studies will become more scientific, interesting, and focused. The most important part of this process is peer review.
Here are some communities where you can reach out for peer review:
Post to any of those, and ask for feedback. You'll get feedback. You'll learn a ton. As experts review your work you will learn a lot about the field. You'll also be practicing a crucial skill: accepting critical feedback.
When I read the feedback on my Pull Requests, first I repeat to myself, "I will not get defensive, I will not get defensive, I will not get defensive." You may want to do that before you read reviews of your Machine Learning work too.
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" AKA "domain knowledge." Learn what you can, for yourself... But you should also collaborate. You'll have better results if you collaborate with domain experts. (What's a domain expert? See the Wikipedia entry, or c2 wiki's rather subjective but useful blurb.)
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.
Quote is from "The UX of AI" by Josh Lovejoy, whole article is a great read!
In other words, You Are Not The User.
Today we are surrounded by software that utilizes Machine Learning. Often, the results are directly user-facing, and intended to enhance UX.
Before you start working ML into your software, you should get a better understanding of UX, as well as how ML and UX can relate. As an informal way to get into this subject, start with this:
Then, if you you know a coworker or friend who works in UX, take them out for coffee or lunch and pick their brain. I think they'll have words of encouragement as well as caution. You won't be an expert by any means, but maybe it'll help you konw if/when to reach out for help, review, or guidance.
Spoiler: you should work with UX specialists whenever you can!
There was a great BlackHat webcast on this topic, Secure Because Math: Understanding Machine Learning-Based Security Products. Slides are here, video recording is here. If you're using ML to recommend some media, overfitting could be harmless. If you're relying on ML to protect from threats, overfitting could be downright dangerous. Check the full presentation if you are interested in this space.
If you want to explore this space more deeply, there is a lot of reading material in the below links:
In early editions of this guide, there was no specific "Deep Learning" section. I omitted it intentionally. I think it is not effective for us to jump too far ahead. I also know that if you become an expert in traditional Machine Learning, you'll be capable of moving onto advanced subjects like Deep Learning, whether or not I've put that in this guide. We're just trying to get you started here!
Maybe this is a way to check your progress: ask yourself, does Deep Learning seem like magic? If so, take that as a sign that you aren't ready to work with it professionally. Let the fascination motivate you to learn more. I have read some argue you can learn Deep Learning in isolation; I have read others recommend it's best to master traditional Machine Learning first. Why not start with traditional Machine Learning, and develop your reasoning and intuition there? You'll only have an easier time learning Deep Learning after that. After all of it, you'll able to tackle all sorts of interesting problems.
In any case, when you decide you're ready to dive into Deep Learning, here are some helpful resources.
Scaling data analysis is a familiar problem now, and there's no shortage of ways to address it. Beware needless hype and companies selling you flashy, proprietary solutions. You can do it all with open-source tools. Even if "buy" instead of "build," you may want to buy from vendors who use known good stacks. No news here.
There are other lists of Awesome Machine Learning tools, so I won't re-do all that. But in the Big Data section, I would be remiss if I didn't mention...
If you are working with data-intensive applications at all, I'll recommend this book:
Lastly, here are some other useful links regarding Big Data and ML.
Here are some other guides to Machine Learning. They can be alternatives or complements to this guide.
pattern_classificationGitHub repository maintained by the author, which contains IPython notebooks about various machine learning algorithms and various data science related resources.