I’m a developer. I have read a book or some posts on machine learning. I have watched some of the Coursera machine learning course. I still don’t know how to get started…Does this sound familiar?
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| Frustrated with machine learning books and courses? How do you get started in machine learning? Photo by Peter Alfred Hess, some rights reserved |
In this post I lay out all of my very best thinking on this topic.
- You will discover why the traditional approach to teaching machine learning does not work for you.
- You will discover how to flip the entire model on its head.
- And you will discover my simple but very effective antidote that you can use to get started.
A Developer Interested in Machine Learning
You are a developer and you’re interested in getting into machine learning. And why not? It’s a hot topic at the moment, and it’s a fascinating and fast growing field.
You read some blog posts. You tried to go deeper but the books are dreadful. Math focused. Theory focused. Algorithm focused.
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| Sound familiar? Have you tried books, MOOCs, blog posts and still not know how to get started in machine learning? |
You may have even attempted some small data sets, perhaps an entry level Kaggle competition.
The problem is you can’t connect the theory, algorithms and math from the books and courses to the problem. There’s a huge gap. A gulf. How ARE you supposed to get started in machine learning?
Machine Learning Engineer
When you think forward into the future, once you have captured this elusive understanding of machine learning, what does your job look like? How are you using your newfound machine learning skills in your day-to-day?
I think I can see it. You’re a machine learning engineer. You’re a developer that knows how to “do” machine learning.
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| Do you want to transition from developer to a developer that can do machine learning? |
Your boss walks over and says:
Hey, you know machine learning, right? Can you use the customer data from last year to predict which current customers in our sales pipeline are likely to convert? I want to use it in a presentation to the board next week…I call this the one-off model.
The problem is well defined by your boss. She gives you the data, which is small enough to look at and understand in MS Excel if you had to. She wants accurate and reliable predictions.
You can deliver. And more importantly, you can explain all the relevant caveats on the results.
Scenario 2: The embedded model
You and your team are collecting requirements from stakeholders on a software project. There is a requirement for the user to be able to freehand draw shapes in the software, and for the software to figure out which shape it is and turn it into a crisp unambiguous version and label it appropriately.
You quickly see that the best (and only viable?) way to solve this problem is to devise and train a predictive model and embed it in your software product.
I call this the embedded model. There are variations (such as whether the model is static or updated, and whether it is local or called remotely via an API), but that’s just detail.
What’s key in this scenario is that you have the experience to notice a problem that is best solved with a predictive model and the skills to devise, train and deploy it.
Scenario 3: The deep model
You have started a new job and the system you are working on is made up of at least one predictive model. Maintenance and the addition of features to this system require an understanding of the model, its inputs and its outputs. The accuracy of the model is a feature of the software product and part of your job will be to improve it.
For example, as a part of regular pre-release system testing, you must demonstrate that the accuracy of the model (when validated on historical data) has the same or better skill than the previous version.
I call this the deep model. You will be expected to build a deep understanding of one specific predictive model and use your experience and skill to improve and verify its accuracy as part of your routine duties.
The Developer That “Does” Machine Learning
These scenarios give you a glimpse at what it’s like to be a developer that knows how to do machine learning. They’re realistic because they are all variations on scenarios I’ve been in or tasks that I have had to complete.
All three of these scenarios make one thing very clear. Although machine learning is a fascinating area, to a developer machine learning algorithms are just another bag of tricks, like multi-threading or 3d graphics programming. Nevertheless, they are a powerful group of methods that are absolutely required for a specific class of problem.
Traditional Answer To: “how do I get started?“
So how do you get started in machine learning?
If you crack a book on machine learning seeking an answer to this question, you’ll get a shock. They start with definitions and move on to mathematical descriptions of concepts and algorithms of ever increasing complexity.
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| The traditional answer to the question “how do I get started in machine learning” is bottom-up. |
There is a reason why machine learning is often taught as a graduate level subject at university. It’s because this “first principles” way of teaching the subject requires years of prerequisites to understand.
For example, it is advisable that you have a good footing in:
- Statistics
- Probability
- Linear Algebra
- Multivariate Statistics
- Calculus
This bottom-up and algorithm fixated approach to machine learning is pervasive.
Online courses, MOOCs and YouTube videos mimic the university approach to teaching machine learning. Again, this is great if you have the background or you’ve already put in your half-to-full-decade of studies to earn those higher degrees. It does not help your average developer.
If you skulk off to a question and answer forum like Quora, StackExchange or Reddit and meekly ask how to get started, you’re slapped with the same response. Often this response comes from fellow developers who are just as lost. It’s one big echo chamber of the same bad advice.
It’s no wonder that honest and hard working developers seeking to do the right thing think they have to go back to school and get a Masters or PhD before they feel qualified to “do” machine learning.
The Traditional Approach is DEAD WRONG!
Think about this bottom-up approach to teaching machine learning for a second. It’s rigorous and systematic and sounds like the right idea on the surface. How could it be wrong?
Bottom-Up Programming (or, how to kill-off budding programmers)
Imagine you’re a young developer. You’ve picked up some of this and that language and you’re starting to learn how to create standalone software.
You tell friends and family that you want to get into a career where you get to program every day. They tell you that you need to do a degree in computer science before you can get a job as a programmer.
You sign-up and start a computer science degree. Semester after semester you are exposed to more and more esoteric algebra, calculus and discrete math. You use antiquated programming languages. Your passion for programming and building software wavers.
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| The traditional approach to getting started in machine learning has a gap on the path to practitioner. |
See the parallels to the teaching of machine learning?
Thankfully, programming has been around long enough, is popular enough and is important enough to the economy that we have found other ways to give budding young (or old) programmers the skills they need to actually do the thing they want to do – e.g. create software.
It does not make sense to load up a budding programmer’s head with theory on computability or computational complexity, or even deep details of algorithms and data structures. Some of this useful material (the latter on algorithmic complexity and data structures) can come later. Perhaps with focused material – but importantly in the context of an engineer that is already programming and delivering software, not in isolation.
Thankfully we have focused software engineering degrees. We also have resources like codecademy where you learn to program by… yep, actually programming.
If a developer wants to “do” machine learning, should they really have to go and spend a bunch of years and tens or hundreds of thousands of dollars to get the requisite math and higher degrees?
The answer is of course not! There is a better way.
A Better Approach
As with computer science, you can’t just invert the model and teach the same material top-down.
The reason is, like a computer science course never making it to the subjects that cover the practical concerns of developing and delivering software, machine learning courses and books fall well short. They stop at algorithms.
You need a top-down approach to machine learning. An approach where you focus on the actual result you want: working real machine learning problems from end-to-end using modern and “best of breed” tools and platforms.
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| A better approach to learning machine learning that starts with working machine learning problems end-to-end. |
written by: Jason Brownlee (countinue)
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