With all this talk these days about Statistics and other frameworks and their immense value in data science, it’s good to be more pragmatic about this matter. After all, it’s not a coincidence that Machine Learning maintains the top position both as a framework and as a specialization when it comes to data science work. In this article, we'll explore why this is.
First of all, machine learning is a more scientific paradigm for data science. It doesn't make any assumptions as it relies on the data at hand and nothing else. Well, there are also the ML models that it makes use of, but it doesn't try to model everything as this or the other distribution and rely on metrics based on these distributions. The scientific approach has proven itself to be very useful in understanding the world, so it only makes sense that it is used (in the form of machine learning methods) in data science too.
What’s more, machine learning makes use of more advanced methods than other frameworks. After all, it makes sense that if a framework works well, as in the case of machine learning, more methods are researched and refined. As a result, the models that machine learning brings to the table are more state-of-the-art and efficient. This makes using the machine learning framework a no-brainer, particularly when it comes to critical processes where accuracy and efficiency are key requirements.
Also, machine learning nowadays is powered to a great extent by AI, creating powerful models that outperform anything else available to a data scientist. This may be a trend that's here to stay since many AI-based model have proven to be exceptionally good and versatile. Although these models have special requirements that may not be met in every data science option, it's good that there is this option available for data science work.
Moreover, machine learning is easier to learn and use since it doesn't have a lot of theory behind it. As a result, you don't need to spend a lot of time learning it or having to worry about the requirements of each model, like in Statistics. Of course, there is some theory in this framework too, but it's fairly straight-forward and doesn't require too specialized math to learn it to an adequate degree.
Finally, there are lots of libraries nowadays for every machine learning model or process, making it easy to implement. In other words, you don't have to do a lot of coding to get your machine learning method up and running. Also, the fact that there is usually adequate documentation in these libraries makes it easier to understand the corresponding programs and the techniques too, supplementing your learning.
Speaking of learning, if you wish to learn more about machine learning through a hands-on approach to the subject, feel free to check out my latest book, Julia for Machine Learning (Technics Publications). There I talk about the subject in some depth, while I explain how you can use Julia to deploy different kinds of machine learning models and heuristics. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.