5 Best Machine Learning (AI) Programming Languages (2022)
For those new to machine learning (ML) and artificial intelligence (AI), it can be difficult to decide where to start. Even those who are already involved in the field may wonder which machine learning programming language is the best. And with over 700 different programming languages widely used, it becomes even harder to decide which is 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 may not be explicitly programmed to tell the difference between a dog and a cat, but it learns to tell the difference on its own 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 then perform actions.
The use cases of a machine learning system dictate the level of programming knowledge needed. If you want to use machine learning to solve real business problems, you’ll need a background in programming. But if you just want to learn the concepts of machine learning, you probably only need knowledge of math and statistics. To implement these patterns, 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, which makes it easy to get started with basic programming languages.
Here are the top 5 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 it and prioritizing it for development. There are a few key aspects that make Python so appealing. For one thing, it’s easy to learn, which is crucial for anyone looking to get into machine learning. It is also scalable and open-source.
Python is a top choice for those looking to perform tasks involving data analysis, data science, machine learning, and AI due to its vast library ecosystem that offers many different libraries for manage, transform and process data. Built-in libraries and packages provide basic code to help machine learning engineers, and they’re available for almost any task. Additionally, libraries and packages have a zero learning curve, which means that once you learn the basics of Python programming, you’re ready to get started.
Some of the best libraries for Python include Numpy, Pandas, Matplotlib, Seaborn, and sci-kit Learn.
The machine learning programming language is used by some of the biggest companies in the world, including Google, Facebook, Instagram, Dropbox, Netflix, Walt Disney, Uber and Amazon.
Here are some of the main advantages of Python:
2. R programming language
Another of the best machine learning and AI programming languages is the R programming language, which can be used by non-programmers as well as programmers. Non-programmers like data miners, data analysts, and statisticians find many uses for R.
R is a top choice for dealing with large numbers, and it’s the language of choice for machine learning applications that use lots of statistical data. Its user-friendly IDEs and tools allow you to draw graphics and manage libraries. It also provides a variety of tools to train and evaluate machine learning algorithms to predict future events.
R is widely used in the fields of bioengineering and biomedical statistics, but it is also popular for implementing machine learning such as 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 advantages of R:
Open-source programming language
Provides a wide range of machine learning techniques
Easy programming style
Very flexible and cross-platform compatible
Another of the best-known programming languages for machine learning, Java has a strong hold on the industry. It’s especially popular among machine learning engineers who have a background in Java development because 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 have large Java code bases, and many open source tools for processing big data 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 integrated machine learning library that provides a collection of algorithms implemented in Java.
Some of the major libraries associated with Java include Weka, JavaML, Deeplearning4j, and ELKI. It also includes Java Virtual Machine which is one of the best machine learning platforms as it allows engineers to write the same code on multiple platforms.
Here are some of the main advantages of Java:
A general-purpose, high-performance dynamic programming language, Julia has emerged as a potential competitor to Python and R. It offers many great features exclusively for machine learning, and although 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 computer science.
Julia supports all types of hardware, such as TPUs and GPUs, and it’s used by big companies like Apple, Oracle, Disney, and NASA.
The machine learning language was particularly designed to implement basic mathematical and scientific queries that make up many machine learning algorithms. Its code is universally executable and scalable, which is fundamental for machine learning engineers.
Here are some of the main advantages of Julia:
Closing our list of top 5 machine learning (AI) programming languages is LISP, which is the second oldest programming language still in use today. It is mainly 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 ability to code, compile and run code in over 30 programming languages. LISP is considered a very efficient and flexible machine learning language for solving specific problems because it adapts to the solution a programmer is coding for, which sets it apart from some of the other high-end languages.
In today’s world, LISP is often used for inductive logic problems and machine learning. He is widely known for creating the very first AI chatbot, and it can still be used today to develop chatbots for industries like e-commerce.
Here are some of the main features of LISP:
Rapid prototyping capabilities
Flexible and highly efficient
Adapts to solutions programmers code for
Support for symbolic expressions