For those just getting started with machine learning (ML) and artificial intelligence (AI), it can be hard to decide where to begin. Even those who are already involved in the field can wonder which machine learning programming language is the best. And with over 700 different programming languages being widely used, it becomes even more difficult to decide the best for a task.
What is Machine Learning?
Machine learning is a subset of artificial intelligence that helps computer systems automatically learn and make predictions based on fed data sets. For example, a machine learning system might not be explicitly programmed to tell the difference between a dog and a cat, but it learns how to differentiate all by itself by training on large data samples. The goal of machine learning systems is to reach a point at which they can automatically learn without human intervention and subsequently carry out actions.
The use cases for a machine learning system dictate the level of programming knowledge needed. If you want to use machine learning to solve real-world business problems, you will need a programming background. But if you want to just learn the concepts of machine learning, you will likely only need math and statistics knowledge. To implement these models, you will need to understand the fundamentals of programming, algorithms, data structures, memory management, and logic.
There are many machine learning libraries with different programming languages, making it easy to get started with basic programming languages.
Here are the 5 best machine learning (AI) programming languages:
Python is by far the most popular and best machine learning language, with over 60% of machine learning developers using and prioritizing it for development. There are a few key aspects that make Python so attractive. For one, it is easy to learn, which is crucial for those looking to get started with machine learning. It is also scalable and open-source.
Python is a top choice for those looking to carry out tasks involving data analytics, data science, machine learning, and AI due to its vast library ecosystem that offers many different libraries to handle, transform, and process data. The in-built libraries and packages provide base-level code to help machine learning engineers, and they are available for almost every task. On top of this, the libraries and packages have a zero learning curve, meaning once you learn the basics of Python programming, you are ready to go.
Some of the top libraries for Python include Numpy, Pandas, Matplotlib, Seaborn, and sci-kit Learn.
The machine learning programming language is used by some of the top companies in the world including Google, Facebook, Instagram, Dropbox, Netflix, Walt Disney, Uber, and Amazon.
Here are some of the main benefits of Python:
Extensive collection of libraries
Easy-to-use and open-source
2. R Programming Language
Another one of the top machine learning and AI programming languages is R programming language, which can be used by non-programmers and programmers alike. Non-programmers like data miners, data analysts, and statisticians find many uses for R.
R is a top choice for processing large numbers, and it is the go-to language for machine learning applications that use a lot of statistical data. Its user-friendly IDEs and tools enable you to draw graphs and manage libraries. It also provides a variety of tools to train and evaluate machine learning algorithms for predicting future events.
R is highly used in the fields of bioengineering and biomedical statistics, but it is also popular for implementing machine learning like classification, regression, and decision tree formation. Some of the libraries for R include CARET for working with classification and regression problems, and PARTY and rpart for creating data partitions.
Here are some of the main benefits of R:
Open-source programming language
Provides wide range of machine learning techniques
Easy programming style
Highly flexible and cross-platform compatible
Another one of the most well-known programming languages for machine learning, Java has a strong hold over the industry. It is especially popular among machine learning engineers who have a Java development background since they don’t need to learn a new programming language like Python or R to implement machine learning models.
Another key aspect of Java is that many organizations already possess large Java codebases, and many open-source tools for big data processing are written in the language. This makes it easier for machine learning engineers to integrate projects with existing code repositories.
Java includes an array of features that make it a great choice, such as ease of use, better user interaction, package services, easy debugging, and graphical representation of data. It has a wide range of third party libraries for machine learning, such as JavaML, which is an in-built machine learning library that provides a collection of algorithms implemented in Java.
Some of the main libraries associated with Java include Weka, JavaML, Deeplearning4j, and ELKI. It also includes Java Virtual Machine, which is one of the best platforms for machine learning as it enables engineers to write the same code on multiple platforms.
Here are some of the main benefits of Java:
Many third-party libraries for ML
Java Virtual Machine
Easy for engineers with Java development background
A high-performance, general-purpose dynamic programming language, Julia has risen to become a potential competitor for Python and R. It offers many great features exclusively for machine learning, and while it is a general-purpose language for the development of a wide-range of applications, it is best used for high-performance numerical analysis and computational science.
Julia has support for all types of hardware, such as TPUs and GPUs, and it is used by big companies like Apple, Oracle, Disney, and NASA.
The machine learning language was particularly designed for implementing basic mathematics and scientific queries that make up many machine learning algorithms. Its code is universally executable, and it is scalable, which is fundamental for machine learning engineers.
Here are some of the main benefits of Julia:
Support for editors like Emacs and VIM
Many predominant features exclusively for ML
Closing out our list of the 5 best machine learning (AI) programming languages is LISP, which is the second oldest programming language still in use today. It is mostly developed for AI-focused applications.
The programming language has led to the creation of various other languages like Python, Julia, and Java. It also has the capability to code, compile, and run code in more than 30 programming languages. LISP is considered a highly efficient and flexible machine learning language for solving specifics since it adapts to the solution a programmer is coding for, which makes it stand out from some of the other top languages.
In today’s world, LISP is often used for inductive logic problems and machine learning. It is widely-known for creating the first-ever AI chatbot, and it can still be used today to develop chatbots for industries like eCommerce.
Here are some of the main features of LISP:
Rapid prototyping capabilities
Flexible and highly-efficient
Adapts to solutions programmers are coding for
Support for symbolic expressions