What Is the Best Programing Language for Machine Learning?
Did you know there are over 700 programming languages that are commonly used? Each one has its own applications and pros and cons. Since machine learning is a rapidly growing field, it can be crucial to learn a language that will work with it.
If you’re trying to figure out the best programming language for machine learning, keep reading. We’ll explore the top five options and help you choose the best one for your goals.
Understand Machine Learning
Machine learning is a division of artificial intelligence. With it, computer systems can learn to make predictions based on the data they are fed. A programmer doesn’t need to write specific code telling the computer what to do. Instead, it learns how to do the task itself without human action.
For example, a computer could identify if an image contained a bird or a cat. In order to do this, it is given large amounts of images labeled as either a bird or a cat. After a while, it learns what each of these looks like and can identify them.
How Much Programming Do You Need to Learn?
How much programming you need to learn to work on machine learning depends on your goals. Some machine learning is quite complicated and requires interdisciplinary skills.
For example, solving real-world business problems using machine learning will require a larger programming background. However, fundamental machine learning is more based on math and statistics.
If you want to build and implement machine learning models, you’ll need to understand the basics of programming, data structures, logic, algorithms, and memory management.
If you have basic programming knowledge, it’s usually easy to start out. This is because many libraries are offered by different programming languages specifically for machine learning. But first, you’ll need to choose a language.
Choosing a Language
There is not one best programming language for machine learning. Each one has areas where it works the best. However, these few are, in general, useful in machine learning.
When you are ready to choose a programming language, you’ll need to think carefully about your programming goals.
Make sure to consider the application and scope of your project and how it will fit with other languages already used in your industry. Some may even wish to experiment with two languages before committing to one for a specific project.
Because of this, we recommend learning at least two programming languages if you are interested in machine learning. Start with one, and once you are proficient in it, add another to your plate. A well-structured course is the easiest way to master a new programming language.
Any of the following would be good languages to start with.
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Python has been steadily growing in popularity for the past five years. A big contributing factor is that it works very well for machine learning and related goals. In fact, it’s the top choice for 69% of machine learning engineers. Even IT giants such as Google, Netflix, and Amazon use Python.
This language is platform-independent, has better readability, and is relatively simple. It has an extensive amount of packages and libraries. This means lower development time and better productivity because the base is already there to start.
Python is also very flexible. It allows programmers to choose an approach that best fits the problem. At the same time, it also reduces the chance of errors.
R Programming Language
R language was written for statisticians by a statistician. However, it can also be used by those in non-programming roles.
Understanding and applying statistical principles is a key part of a machine learning engineer’s job. R language has tools that help engineers apply algorithms and evaluate them for future use.
However, it can also use a variety of techniques. This means whether your program is based on data sampling or model evaluation, R will be able to work.
This language is relatively easy and very flexible. It’s open source, making it cost-effective.
Java is popular among other programming languages. Because of this, those who already know it are also using it for machine learning. It also helps integrate organizations that are already using Java as their bases.
Using Java for machine learning has a few benefits. It’s easy to use and debug, the user interaction and package services are good, and the way data is represented graphically makes sense. These features also make it a very scaleable option. Java can even be used to build complex and large applications from the ground up.
The language executes quickly, making it perfect for machine learning projects where speed matters.
There are many third-party libraries for machine learning available for JAVA. The Java Virtual Machine is an ideal platform to use. The same code can be written on multiple platforms.
Julia is a newer language that has emerged as a competitor with Python, and R. Many of its original features are exclusively meant for machine learning, and the language is high-performing and dynamic.
However, Julia is also enough of a general-purpose language that it can be used in various applications. This is especially true for uses dealing with high-performance computational science and numerical analysis.
Julia is already being used at big organizations such as NASA and Disney due to its support of all kinds of hardware. It is highly scalable, making it a great choice for various organizations.
LISP is the second-oldest programming language that is still in use. Originally created in 1958, it was meant to be used for AI purposes. This language is highly efficient and flexible, running code using over 30 languages.
The language adapts to the application, making it perfect to use with inductive logic problems, chatbots, and more.
Unfortunately, LSIP is difficult to learn and does not have many users. It also has little support for the most common machine learning libraries.
Which Programming Language Will You Learn for Machine Learning?
Python, R, LISP, Julia, and JAVA are all key programming languages for machine learning. Since they all have their benefits and downsides, you’ll need to choose the language that best fits the needs of your project and organization. Then you’ll be ready to get started learning your next programming language.
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