Python.

Ok thanks, see you next week.

…Still here? Okay fine, I’ll elaborate. And I’ll consider the possibility that I might be a bit biased.

In all seriousness, choosing the right programming language for machine learning is kind of like picking the perfect pair of shoes - you’ll want something that fits your needs, feels comfortable, and doesn’t give you blisters (aka endless debugging).

I get it, it can get overwhelming with so many options.

But when it comes to choosing a language (or anything related to ML, honestly), it’s wise to always consider your specific needs/goals and then see which language fits best.

Let’s break down some of the most popular options.

There is a reason why this is first on the list.

Python is by far the most popular language in machine learning, and the one I recommend learning (or if you already know it, expanding upon) for ML.

Python is simple, readable, and arguably most helpfully, it has an insane number of libraries like TensorFlow, Sci-Kit Learn, and PyTorch that do a LOT of the heavy lifting for you.

If you already know Python, then kudos…you’re already off to a good start in your ML journey. If not, no worries: it has an easier learning curve than most other languages.

Python also comes with the benefit of having massive community support. Think of Python as the chill genius everyone loves to work with.

Are you a numbers person? Love data visualizations?

If so, R just might become your best friend.

R is great if you’re coming from a statistics or data science background. Think of that nerdy friend who’s a math prodigy who calculates everything, and provides charts full of numbers and statistics.

R is a language built specifically for statistical computing.

If Python is the all-around star athlete of ML, then R is like the brilliant statistician who prefers numbers over the small talk.

If your specific ML goals revolve around data science, where you need to dig deep into your data, R is your go-to. It is amazing for analysis!

However, it is not as intuitive as Python, and thus not as ideal for scalability.

C++ is like the Formula 1 car of programming languages—super fast, but not beginner-friendly. It’s used when performance really matters.

Things like embedded systems, robotics, and ML frameworks like TensorFlow utilize C++ because these are all examples where speed and performance are a real factor.

Plus, it also gives you control over memory.

If your needs in ML require raw speed (Ex. real-time vision systems, game AI), you may want to consider this GigaChad of a programming language.

Java is reliable, robust, and great for large-scale systems and enterprise applications.

It’s not as flashy as Python, but it’s certainly dependable. It’s used in big companies where scalability matters—think banks, insurance, or anywhere a crash could cost millions.

It’s the buttoned-up, rule-following professional who can crunch data, train models, handle big data processing.

If you’re working with big, production-level ML systems, or large codebases, Java is the professional who will show up for the job.

Imagine if C++ and Python had a genius baby.

Enter Julia.

Julia is like the new kid on the block, who’s naturally brilliant and fast. It’s got almost the same speed as C++, while also being readable like Python.

However, just like any new kid, it’s still maturing.

It’s got a smaller community and fewer libraries and resources.

However, it’s great for tasks that need math-heavy work and scientific computing, without sacrificing speed.

As you can see, choosing the right programming language depends on your specific goals and needs.

Need a language that has a huge library and is easy to learn? Python. Need one that specializes in data and statistics? R. Fast and powerful? C++. Scalability? Java. Speed and great at computing? Julia.

However, at the end of the day, I’d still recommend Python.

Python is the friendly neighborhood guide who will give you all the right tools to have the best possible start to ML, and has a massive community of support ready to help when you need it.

With tons of tutorials, tools, and help at your disposal, you can’t really go wrong with it.

Feel free to reply to me directly if you would ever want me to cover Python for Machine Learning.

Until then, that’s a wrap…I’ll see you at the next data drop!