As you may already know, Julia is a functional programming language geared towards scientific computing. It is particularly useful in data science nowadays as there are many specialized libraries for this. Simultaneously, it's a fast and easy-to-work-with language, enabling you to create useful scripts for data science tasks quickly. Additionally, it's similar to Python while there are bridge packages for the two languages, making it possible to jump from one to another, leveraging code from both languages in your data science projects.
As for machine learning, this is the part of data science that deals with the creation, refining, and deployment of specialized data models, based on the data-driven approach to data analytics. It involves systems like K-means (for clustering), Support Vector Machines (for predictive analytics), and various heuristics (for specific tasks such as feature evaluation) to facilitate all kinds of data science work. Much like Statistics, it is versatile, though contrary to Stats, it doesn't rely on probabilistic reasoning and distributions for analyzing the data. That's not to say that you need to pick between the two frameworks, however. A good data scientist uses both for her work.
Julia and machine learning are a match made in heaven. Not only does Julia offer direct support for machine learning tasks (e.g., through its various packages), but it also makes it easy for a data scientist (having just basic training in the language) to write high-performance scripts for processing the data at hand. You can even use Julia just for your data engineering tasks if you are already vested in another programming language for your data models. So, it's not an "either-or" kind of choice, but more of an add-on situation. Julia can be the add-on, though once you get familiar with it, you may want to translate your whole codebase in this language for the extra performance it offers.
This prediction isn't some optimistic speculation, by the way. Julia has been evolving for the past few years at a growing rate, even though other programming languages have also been coming about. Furthermore, it has the backing of a prestigious university (MIT), while there is a worldwide community of users and Julia-specific events, such as JuliaCon, happening regularly. So, if this trend continuous, Julia is bound to remain relevant for the years to come, expanding in functionality and application areas. Naturally, if machine learning continues on its current trajectory, it will also stick around for the foreseeable future.
If you want to learn more about Julia and machine learning, especially from a practical perspective, please check out my book Julia for Machine Learning, published in the Spring of 2020. There, you can learn more about the language, explore how it's useful in machine learning, learn more about what machine learning entails and how it ties in the data science pipeline, and experiment with various heuristics not so well known (some of them are entirely original and come with the corresponding Julia code). So, check this book out when you have the chance. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.