The most difficult part of learning machine learning if you’re new to it is deciding where to start. It’s only natural to wonder which language is best for a machine learning project, whether you’re looking to brush up on your skills or start a new career in the field. Finding the best programming language for machine learning is challenging because there are over 700 different programming languages in use, each of which has advantages and disadvantages. However, the good news is that as you begin your career as a machine learning engineer, you will begin to determine which programming language is best suited to a specific business issue.

However, first things first: let’s learn what machine learning is and how much programming is required to implement it.

How does Machine Learning Work?

Computer systems are given the ability to automatically learn and make predictions based on the data they are fed through machine learning, which is a subset of artificial intelligence. Anything could be a prediction: whether the word “book” means making an appointment or a paperback, whether an image has a cat or a dog, or whether an email is spam. The code that tells a machine learning system how to distinguish between an image of a cat and a dog is not written by a programmer in machine learning. Instead, large samples of data are used to train machine learning models that learn to tell the difference between a dog and a cat (in this case, a large number of images labeled as cat and dog). The ultimate objective of machine learning is for systems to learn on their own and carry out action by what they learn.

There is no best machine learning language; each is useful in its way. Yes, no one machine-learning language is superior to others. However, there are a few programming languages that are better suited to machine learning projects than others. Depending on the kind of business problem they are working on, machine-learning engineers select a machine-learning language. For example, the majority of engineers working in machine learning prefer to use Python for NLP issues and R or Python for sentiment analysis tasks. Others, on the other hand, are likely to use Java for other machine learning applications like security and threat detection. When working in machine learning, software engineers with a background in Java development may occasionally continue to use Java as the programming language.