The term “artificial intelligence” often conjures up images of robots taking over the world, but the reality is that AI is already a part of our everyday lives. From the GPS in our cars to the recommendations we receive on Netflix, AI is constantly being used to make our lives easier. In the future, AI will become even more prevalent as it becomes better at completing tasks without human intervention.
As machine intelligence continues to evolve, the potential applications for AI will only become more numerous and impactful. With the rapid expansion of artificial intelligence (AI) and machine learning (ML), businesses are looking for developers with the skills to create programs that make these technologies work. But which programming languages should you learn if you want to develop AI or ML applications?
Here is a list of six of the best programming languages for AI and machine learning development, based on our findings: Python, R, Julia, Prolog, Haskell, and Lisp.
Table of Contents
Python is a versatile programming language that you can use for building all sorts of applications, including those with AI and ML components. It’s easy to learn for beginners and has many libraries and frameworks that support development in these areas.
Data engineering and machine learning are two frequent use cases for Python. Data engineering involves working with data to enable it to be used for effective decision-making, while machine learning involves using algorithms to train machines.
TensorFlow is a popular framework for AI development in Python. It’s a free machine learning library that allows you to create deep neural networks. TensorFlow is used by many organizations, including Twitter, Dropbox, and Airbnb. In addition to being a popular language for AI development, Python is also widely used in web development, scientific computing, and system administration. Python is a versatile language that can be used for a variety of tasks.
There are many other libraries for Python for AI and ML development as well, including:
- Pandas is a library that provides high-performance, easy-to-use data structures and data analysis tools.
- SciPy is a library that provides mathematical algorithms and tools for scientific computing.
- nltk is a library that provides Natural Language Processing (NLP) tools.
- PyTorch is a library for deep learning that offers both high flexibility and speed.
- Keras is a high-level neural networks API that supports both convolutional networks and recurrent networks.
- Theano is a numerical computation library that lets you define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
- scikit-learn is a library that provides simple and efficient tools for data mining and machine learning.
R is a programming language that is particularly well-suited for statistical computing and data analysis. It has many libraries and tools for data manipulation, calculation, and graphical display.
R is a popular language for machine learning as well. In fact, it was developed in part for this purpose. R’s syntax is designed to be easy to read and understand, which makes it a good choice if you’re just starting out with machine learning.
Data is crucial in AI development, so those who want to analyze and represent data accurately must be willing to get a bit mathematical. R can be a better solution in this case. In terms of getting figures correct, R is actually superior to Python.
Those who learn to use R effectively will be able to accurately represent data, which is essential for developing successful AI models.
If you’re looking for some R packages to help with your data analysis, you may want to check out Gmodels and Tm. Gmodels provides tools for model fitting, while Tm is a framework for text mining applications. OneR is another library that can also be useful for implementing One Rule Machile Learning classification methods.
Julia is a programming language that is designed for scientific computing. It is a relatively new language for AI development.
It aims to provide high performance while being easy to use. Julia has many features that make it a good choice for AI development, including dynamic dispatch, multiple dispatches, and an extensive mathematical function library.
It comes with premade models and algorithms, as well as multiple packages for probabilistic programming, making it a great choice for AI development. Julia also has great community support, with active forums and a thriving GitHub community. While it’s not the newest language on the market, Julia’s features make it well worth considering for AI development projects.
Julia also provides excellent support for parallel computing, which can be important for training large machine learning models.
There are many libraries available for Julia that can be used for AI and machine learning development, including: MLJ.jl, Flux.jl, Turing.jl, Metalhead.jl, and Knet.jl. Each of these libraries has its own strengths and weaknesses, but all of them provide a wealth of possibilities for developing AI and machine learning applications in Julia.
Prolog is a logic programming language that has been around since the 1970s. Rather than using coded sequences, Prolog relies on a basic set of facts, rules, goals, and queries.
One of Prolog’s main advantages is its ability to handle complex relationships between data. This can be extremely useful for tasks such as natural language processing and knowledge representation.
The basic tenets of Prolog are as follows: facts, rules, goals, and queries. Each of these components is essential for understanding how Prolog works.
- Facts are the starting point for any statement; they establish what is true.
- From there, rules can be used to add complexity and detail to the statement.
- Goals provide a way to test whether a given statement is true.
- And queries allow for the exploration of different ways to make a statement true.
Prolog is particularly well-suited for applications that require search, pattern matching, and symbolic reasoning. Its syntax is designed to be easy to read and understand, which makes it a good choice if you’re just starting out with machine learning.
Haskell is a functional programming language that is particularly well-suited for AI development. It is known for its static typing, which can help to prevent errors in code. Haskell also has a strong type system, which can make it easier to develop safe and reliable AI applications.
Haskell’s declarative style of programming makes it easier to read and understand code, even for those with little experience. This makes it a good choice if you’re just starting out with machine learning.
Haskell also provides excellent support for parallel computing, which can be important for training large machine learning models.
Lisp is one of the oldest programming languages, having been first developed in the late 1950s. Lisp has a simple syntax that can make it easy to read and understand code. It also has a powerful macro system that allows for great flexibility when developing AI applications.
Due to its age, Lisp has a large community of developers and a wealth of resources available. This can be extremely helpful if you’re just starting out with machine learning.
AI projects can often benefit from the use of a Lisp programming language. This is due to Lisp’s specialization in a few areas, including rapid prototyping, dynamic object creation, executing data structures as programs, modifying programs as data, and mandatory garbage collection.
In each of these areas, Lisp can help speed up development time or improve code quality. For example, its garbage collection feature automatically cleans up unwanted data, which can be a boon for large-scale projects that generate a lot of data.