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Top 5 AI Programming Languages for AI/ML App Development
Let’s move straight to the top 5 capable AI Programming languages that can turn your AI App Ideas into jaw-dropping apps.
Python
When it comes to NLP (Natural Language Processing) or Sentiment Analysis, people normally prefer Python for AI/ML development services as it is easy to learn.
In fact, Python is the most popular AI programming language among the developers, holding 31.73% of the community share. It offers packages for General AI, Natural Language Processing, Machine Learning, and Neural Networks.
Src: http://pypl.github.io/
Suppose you selected data science as a profession, in that case, you don’t want to focus so much on the AI programming language. You just want to implement your idea, but how will you implement that!
In such a case, you have to learn an AI programming language.
The easiest language to learn is Python.
And, last but not least, Python is a great choice for Artificial Intelligence in Web Development.
Some benefits of Python for AI Programming:
- Easy to Learn
- Huge Library & Framework Ecosystem
- Truly Flexible
- Readability
- Platform Independence
- Good Data Visualization
- Community Support
We have seen Python is a language that is easy to learn and offers so many libraries like:
- TensorFlow
- Pytorch
- Keras
- SparkMLlib
- Pybrain
- Scikit-learn
- Theano
- OpenCV
- NumPY
Java
Java is the second most trending programming language that is helping AI developers with Search Algorithms, Neural Networks, and NLP (natural language processing).
For example, If you change the domain from data science to enterprise, you might be working on Big Data, Fraud Detection, or you will be working on Network Security, in this case, your team could force you to go for Java.
Java is worth-mentioning and good when it comes to implementing AI programming for Next-Gen Apps, and it also offers other benefits for app development like:
- Easy to Use
- Packaged Services
- Easy Debugging
- Platform Independence
- Good for Large Projects
- Built-In Garbage Collector
- Graphical Representation of Data
Libraries offered by Java for AI programming is also a benefit. It offers these libraries:
- Apache Jena
- Eye
- d3web
- PowerLoom
- Tweety
- ApacheOpenNLP
- Jenetics
- Acceleo
C++
C++ is one of the fastest programming languages that are prominent to use for AI/ML development services. According to a survey from StackOverFlow, it is the 9th most popular technology among developers:
Src: insights.stackoverflow.com
This language is also good for writing resource-intensive applications when proper use of resources, and performance is essential. It helps you to save the overhead cost of JVM when people are working in an embedded development environment.
Assume, you are focusing on Robots, Speech Recognition, Computer Vision, Games, or AI-enabled devices for healthcare, the widely used language is C++.
We have already been making games using C++ because we don’t want to make it heavy using JVM concepts or interpreters. So, it makes sense to use C++.
The most benefits that C++ offers for AI app development:
- Object-Oriented Programming
- Portability
- Proper Use of Resources
- Faster Execution of Code
- Low-Level Manipulation
- Multi-Paradigm
- Memory Management
- Scalability
Libraries that are offered by C++ to make AI programming easier are:
- Shark
- MLpack
- Boost
- Torch
- TensorFlow Lite
R LANGUAGE
R is a statistical language that is a perfect fit for the recent in-crunch AI programming. It is made for manipulating data to generate statistics. So, if you are up to generate valuable business insights using Artificial Intelligence, then R will be a perfect choice.
It is made for Data Science and Deep Learning where you can utilize it for Predictive Analysis, Statistical Computations, Numerical Analysis, Neural Networks, and Machine Learning.
You don’t need to be a Code Nazi to use R.
If you are someone who knows the basics of Python, Java, C++, or JavaScript, then R will be easy to implement.
Implementation of R can be done in domains like Finance, Healthcare, eCommerce, Banking, and Consulting.
Src: stackoverflow
Benefits that R comprises for AI app development:
- Free and Open-Source
- Machine Learning Operations
- Big Data Compliant
- Strong Package Ecosystem
- Variety of Statistical Solutions
- Easy to Integrate
- Highly Compatible
- Platform independent
As R is a strong package ecosystem for machine learning, there are a number of R packages for AI/ML development services:
- Carat
- Random Forest
- RPart
- DataExplorer
- Dplyr
- MICE
- E1071
- Plotly
JavaScript
If you are a web developer and want to try a hand in AI programming, then it is possible. You might be familiar with JavaScript, don’t you?
But, is it even possible to use JavaScript for AI programming?
Yes, it is! It might sound like an unusual choice. But, you can use JS for AI programming.
You don’t need to hire a python developer. You can get a head-start with your JS skills.
It can be used for training Neural Network, Deep Learning, Web & Mobile app development, and other AI development activities.
Using TensorFlow.js open-source library, you can define, train, and run machine learning models entirely in the browser and Node.js.
JS offers multiple benefits that make it good for AI/ML development services:
- Security
- Fast Performance
- Less Time-to-Market
- Cross-Platform Programming
- Offers Great Pool of Libraries
- Scalable Applications
There is a vast range of JS Libraries & Frameworks for AI app development that can be used:
- TensorFlow.js
- ConvNetJS
- Brain.js
- Synaptic
- Neuro.js
- WebDNN
- DeepForge
- Ml.js
