You might have heard of machine learning. The world seems to have been enveloped with hype around the topic, with everything from self-driving cars to human-like robots getting highlighted. As a Java developer, you might have felt a bit left out: most of the popular data science and machine learning frameworks are built for Python first. How can you get involved with machine learning as a Java developer?
I work with Springboard, which offers the first machine learning career track with a job guarantee. We’ve spent a lot of time working with applicants with strong Java backgrounds and helping them bridge the gap between that background and getting into machine learning positions: whether as a software engineer who can add machine learning into their toolkit, or as somebody who works as a machine learning engineer full-time.
Here’s a step-by-step tutorial specifically tailored to Java developers to do just that.
1- Understand machine learning fundamentals
It doesn’t matter what programming language you start with, you have to understand the statistical and theory-based foundation of machine learning in order to properly implement it. A good place to start are Google’s courses on deep learning and reading up on how machine learning algorithms work. If you need a math, stats and probability refresher, KhanAcademy has helpful resources on the linear algebra, probability and statistics and calculus you’ll need to refresh on to understand what you’re doing with machine learning models.
2- Practice implementing machine learning models with Java workbenches
You need to have an environment that has machine learning frameworks with a GUI (graphical user interface) that allows you to interact directly with machine learning algorithms in an intuitive fashion. Weka, for example has a series of implementations of machine learning that are pre-written in Java. Pick and choose from a list of GUIs with Java APIs and get comfortable working with machine learning in Java.
3- Work with different Java machine learning libraries
Once you have worked in a Java environment friendly to machine learning, you should then go on and practice with different Java frameworks specifically written to implement certain machine learning algorithms. Make sure you get some practice in with the libraries best suited to your machine learning needs.
4- Deploy your first projects
At the end of the day, machine learning is about taking ML models and applying them to data in a way that is scalable and performant. You’ll want to take the theory you’ve learned and apply them to different problems. If you need datasets to play with, take a look at Kaggle. Not only does the platform offer you the opportunity to compete and demonstrate your machine learning skills, it also offers datasets that are rated by their popularity, and contextualized with different projects that other people have built on them.
If you can’t find what you’re looking for on Kaggle, this Github repository has links to all sorts of awesome public data resources you can experiment with.
5- Advance your career with your new skills
Finally, once you’ve built a few projects and worked with different machine learning frameworks, you are ready to start networking with ML engineers and applying your new skills in your current job or perhaps in a new job as a machine learning engineer. Get up-to-date on machine learning interview questions and get advice on how to use your new machine learning skills as a software developer. One successful way to network is to conduct informational interviews with machine learning experts who work at the companies you would like to work for. You can find those experts on LinkedIn, for example, and reach out there.
Hopefully this list has helped you craft a roadmap to go from Java developer to machine learning engineer or a Java developer with a machine learning toolkit. If you feel like you need professional mentorship and a job guarantee to ensure you’re going to get into machine learning, look no further than Springboard’s AI/Machine Learning Career Track.
