7 Top Programming Languages that are Widely Used in AI/Machine Learning Projects
Today, we will discover the different programming languages that are being used in AI/Machine Learning projects. We will also learn the use of programming language in AI/Machine Learning projects.
Let’s quickly start this tutorial by exploring one news of the market situation.
There is a lack of skilled Artificial Intelligence/Machine Learning professionals in the market. Here is a graph that shows that rise in the demand for AI/machine learning professionals.
The above job posting does not disclose the complete picture. According to the recent report, there has been a 34% growth in AI/machine learning patents. Apple, Google, Microsoft, and many other tech giants are pouring money in AI.
Now, let’s check some facts and news. According to CNBC, AI will end 1.8 million jobs shortly but on the other side, AI/Machine Learning will also add 2.3 million jobs in the market.
It is a great time to kickstart your career in AI/machine learning field. Skills in this field will help you build a strong foundation to perform efficiently in an organization.
One of the core skills that you need in AI/Machine Learning field is a programming skill. As of now, there are thousands of programming languages. Don’t worry, you don’t need to learn all of them.
Programming Languages in AI/Machine Learning
Not all programming languages are equally important. Out of thousands of programming languages, only a few survive and serve the community.
There was an interesting discussion among experts on Berkley University about why some programming languages survive and why some others die. They came out with some points that contribute to the end of programming languages:
- It takes time to learn a programming language, and the programming community will not entertain a new language with no real uses.
- Too much-sophisticated features that confuse developers.
- Poor Documentation
- When a programmer gets experienced, they are more committed to solving problems rather than learning new languages.
The programming languages that we are going to reveal now have survived the test of time. They are in use for numerous types of projects for decades. These programming languages are making a significant impact in the field of AI/Machine learning. Here are the programming languages that you must consider learning:
Python programming language has been developing in both the aspects; features as well as popularity since its introduction. Here is a graph that demonstrates its growth.
The growth in popularity of Python is because of its flexibility, scalability, and simplicity along with its wide range of features and libraries. Python focuses on agile development. AI and machine learning professionals like Python because of its simplicity. They do not have to think long and hard to write codes for sophisticated machine learning problems.
It is the first language that you must learn and also the most preferred programming language in data science, AI, and machine learning if you want to dive into the AI/Machine Learning sector. There are numerous libraries to solve the majority of data science and AI/Machine Learning problems, such as AIMA, Pybrain, Numpy, etc.
It is also widely in use in the web development space.
2. R Programming
R Programming is a language that is specifically for solving numerical/statistical problems. It was not so popular when it was launched. However, the growth of data science, machine learning, and AI have dramatically risen its popularity. It is because data science, AI, and machine learning need huge numerical features and libraries that R have in its arsenal.
You can find a massive set of libraries and functions for solving problems related to data analysis, sampling, supervised learning, and model evaluation. You can get started for free (open-source), and a lot of academicians prefer R for their projects.
C++ is one of the fastest languages due to its ability to transfer its message at a hardware level. It is a programming language for time-sensitive AI/machine learning projects. It works great with statistical AI approach, which is a part of neural networks.
C and C++ were also used for the development of numerous machine learning/deep learning libraries.
C++ gives you a firm hold over runtime and performance. You can use its templates safely and for generalizing APIs.
Java is a programming language that has touched many segments of the IT industry, including AI and machine learning. You can use Java Virtual Machine technology to create a single-version app. There will be no problem to run it in Java-supported machines.
Java has excellent support for large projects (which is essential in AI/Machine Learning), facilitated visualization, easy debugging, smooth user interaction. You can easily find help online, thanks to its vast online community.
Some of the well-known Java applications are:
- WEKA machine learning suite
- JOONE neural engine
- ALICE (AI application)
You can find many libraries to perform AI/Machine Learning tasks. Some of the popular ones are:
- ConventJS for Deep Learning implementation
- Synaptic for developing and training neural networks
- Mind for training data
Julia allows you to deal with top performance numerical and computational analysis; there is no requirement for separate compilation. The deep mathematical roots combined with greater customizability make it easier for AI/Machine learning professionals to work with it. One can easily translate the problem into an algorithm without spending a lot of time.
Julia is compatible with all the major hardware like IBM, Intel, ARM, and Nvidia. You will find its syntax similar to Python, R, and MatLab and it has great speed like C++. It eliminates the need for model estimation in one language and produces the need in another because of its ease and speed. Many huge corporations use Julia for their projects.
In 2016, the developers used Julia for deep learning solutions for better eye diagnosis in rural parts of India.
The final language on the list is Haskell. It is a robust static typing language that has been around since 1990. There are reputable organizations that use Haskell for their projects, however, it is more popular in academic circles.
Haskell language offers support for embedded domain-specific languages, which is crucial for AI research. It is also great for abstract mathematics and supports efficient libraries for the development of AI algorithms. It uses common algebraic structures, such as monoids and modules for enhancing the efficiency of Machine Learning algorithms.
Haskell is also superb for probabilistic programming that is important for AI/Machine Learning developers to identify errors in the compilation stage of the iteration quickly.
Over to You
There are tons of benefits of learning a programming language. It will increase your chance of landing a job in AI/Machine Learning field. Besides that, you can apply that skill in various other fields as well, such as data science, enterprise software development, web development, etc.
You do not need to learn all programming languages in AI/Machine Learning at once. First, learn one language and then jump into another. After knowing one programming language, it will not be difficult to add another one. We would recommend starting your journey with Python.
Any other programming languages in AI/Machine Learning that you want to add? If yes, feel free to share your views in the comment below.