Best Programming Languages for Artificial intelligence, Machine Learning and Deep Learning 

 Best Programming Languages for Artificial intelligence, Machine Learning and Deep Learning

 

1. Python

When it comes to AI programming languages, Python leads the pack with its unparalleled community support and pre-built libraries (like NumPy, Pandas, Pybrain, and SciPy) that help expedite AI development. For example, you can leverage proven libraries like scikit-learn for ML and use regularly updated libraries like Apache MXNet, PyTorch, and TensorFlow for DL projects.

For Natural Language Processing (NLP), you can go old school with NLTK or take advantage of lightening-fast SpaCy. Python is the leading coding language for NLP because of its simple syntax, structure, and rich text processing tool.

However, while it’s sometimes referred to as the best programming language for AI, you’ll have to look past its five different packaging systems that are all broken down in different ways, some white spacing issues, and the disconnect between Python 2 and Python 3.

But in the grand scheme of things, it makes perfect sense to learn Python, as it boasts the most comprehensive frameworks for both DL and ML. As this highly flexible AI language is platform agnostic, you’ll only have to make minor changes to the code to get it up and running in a new operating system.

(Use this free curriculum to build a strong foundation in ML, with concise yet rigorous and hands-on Python tutorials.)

2. Java

We can’t discuss the best programming language for AI without talking about the object-oriented programming language, Java. Since it first emerged in 1995, Java has grown to become a highly portable, maintainable, and transparent language that’s supported by a wealth of libraries.

Like some of the programming languages on this list, Java is also highly user-friendly, easy to debug, and runs across platforms without the need to engage in any additional recompilation. This is because its Virtual Machine Technology allows the code to run on all Java-supported platforms.

When it comes to working with NLP, it’s easy to find enough support from the vibrant community that’s built around it. As Java enables seamless access to big data platforms like Apache Spark and Apache Hadoop, it has cemented its place within data analytics-related AI development.

If you need more reasons to learn Java, consider the fact that it works seamlessly with search engine algorithms, improves user interconnections, and its simplified framework supports large-scale projects efficiently.

3. Julia

Whenever a task demands high-performance numerical computing and analysis, Julia (developed by MIT) will be the best programming language for AI projects. Explicitly designed to focus on the numerical computing that’s required by AI, you can get results without the typical requirement of separate compilation. Its core programming paradigm includes a type system with parametric polymorphism and multiple dispatch capabilities.

Unlike the languages above, Julia isn’t exactly the go-to language right now. As a result, it’s not supported by a wealth of libraries or a rapidly growing community.

However, as an open-source language (under a liberal MIT license), its popularity is slowly increasing. Wrappers like TensorFlow.jl and Mocha provide excellent support for DL, so there is help out there—just not the same amount as Python.

One of the primary benefits of working with Julia is its ability to translate algorithms from research papers into code without any loss. This significantly reduces model risk and improves safety.

Engaging in AI programming with Julia reduces errors and cuts costs because it combines the familiar syntax and ease of use of languages like C++, Python, and R. This negates the need to estimate a model in one language and reproduce it in a faster production language.

4. Haskell

Haskell is a standardized strong static typing (general) language developed in the 1990s with non-strict semantics (based on the Miranda programming language).

Its popularity is primarily concentrated in academic circles, but tech giants such as Facebook and Google have also been known to use it. Haskell is used in research projects because it supports embedded domain-specific languages that play a significant role in programming language research and AI.

Unlike Java, Haskell is perfect for engaging in abstract mathematics, as it allows expressive and efficient libraries to create AI algorithms. For example, HLearn leverages common algebraic structures like modules and monoids to express and accelerate the speed of simple ML algorithms.

While you can code these algorithms in any AI language, Haskell makes them far more expressive than others while maintaining an acceptable level of performance.

It’s also an excellent host for probabilistic programming and helps developers quickly identify errors during the compile phase of the iteration. As Haskell isn’t very popular in enterprise environments, you can’t expect the same level of support enjoyed by the likes Java and Python.

5. Lisp

While AI has only started making a significant consumer impact in recent years, research and development within this field goes back as far as the 1950s. Since the early days, Lisp has been at the heart of AI development, and that remains true today.

Some consider it to be the best language for AI because it was created by computer scientist and father of AI John McCarthy in 1958. It’s also highly suited for AI development because its unique features enable the effective processing of symbolic information.

Lisp can be described as a practical mathematical notation for computer programs. AI developers often turn to Lisp for AI projects that are heavy on ML because it offers rapid prototyping capabilities, support for symbolic expressions, a library of collection types, and is highly flexible and adaptable to their problem-solving needs.

It’s also popular among AI programming languages because it allows the easy dynamic creation of new objects, with automatic garbage collection. While the program is still running, you can also enable interactive evaluation of expressions and recompilation of functions or files concurrently.

However, in recent years some of the key features that made it special have migrated into several other languages, so it’s no longer as unique an option in the world of AI.