Machine Learning: Python vs. Java

You can hardly say the phrase ‘machine learning’ without conjuring up images of arcane mathematics, powerful algorithms, and cutting-edge technologies. And these are indeed characteristic of the field. But whether you learn on your own or at a data science bootcamp, machine learning is also a concrete way to do high-impact work that’s exciting, challenging, and rewarding. 

Given all this, it isn’t surprising that there’s been a growth in the number of machine learning jobs and the enthusiasm for them in the popular imagination. If you want to prepare for a career in this branch of artificial intelligence, a good place to start is to review your options for which programming language to learn first. There are two major contenders, and in this article we’re going to discuss Python v.s. Java for machine learning.

Python for Machine Learning

Without a doubt, one of the most popular languages for machine learning (and everything else) is Python. 

Python seems to be one of the favorite general-purpose languages for tasks ranging from backend web development to finance to modeling the climate. And for good reason! Python has an elegant, aggressively simple syntax which is famously easy for complete beginners to learn and understand. But it’s also powerful, with an array of third party libraries for almost every conceivable task. 

It’s no surprise, then, that Python has found widespread adoption in the machine learning community. Using Python for machine learning can boil down to building actual algorithms in raw Python, or doing the same thing with a popular library like Sklearn or TensorFlow.  

As part of the Galvanize Data Science Immersive, I carefully studied several ways of using Python to build neural networks from scratch. This taught me a lot about the nuts-and-bolts of neural networks, which I wouldn’t have learned if I had let a software package do all the heavy lifting for me. 

While working on my Capstone, I utilized both Sklearn and TensorFlow to build much bigger and more powerful neural networks to generate text. 

I can say first hand that Python significantly reduces the pain associated with doing machine learning. Learning Python as a start to becoming a machine learning engineer is a great choice. 

Java for Machine Learning

Java is one of the oldest and most popular languages in widespread use today. One reason to use Java for machine learning, therefore, is simply because there is so much of it around. Many companies have huge Java codebases, and much of the open-source stack for processing big data is written in Java. This means that Java-based machine learning projects will likely be easier to integrate with existing repositories. 

Like Python, there are also plenty of 3rd party Java libraries for machine learning. Deeplearning4j allows for the creation of any kind of neural network, and furnishes support for popular algorithms like linear regression and k-nearest neighbors. If you don’t like that, you could also go with Neuroph, and alternative Java library for neural networks. Hyperparameter tuning–a fancy phrase which points at an exceptionally important part of getting machine learning algorithms to run effectively–can be handled with the Arbiter Java library. 

Should I Learn Python or Java for Machine Learning? 

My personal verdict is that you should use Python for machine learning, but there is absolutely a case to be made for going with Java. Of course, the best thing to do would simply be to learn both. Once you are proficient in one language, learning another is much easier. And having command of two of the most popular machine learning languages will doubtless help put your resume at the top of the stack!