The Top 5 AI Programming Languages You Need as an AI Engineer

Despite how new the technology is, about 83% of businesses claim that artificial intelligence is currently a top priority in their business plans.

AI is exploding in popularity and it’s entering all kinds of industries. As it stands, however, most people still don’t have the strongest understanding of it. This is in terms of using it, developing it, and implementing it.

Depending on your career goals, AI could be incredibly important. You need to learn about the best AI tools for your career in detail if you want to get ahead of the competition.

In this guide, we’ll take a look at the best AI languages to learn if you want to achieve your goals. Keep reading for more.

1. Python

Programming languages have been around for a while now, and Python is one of the most well-known. It has become particularly prominent in the world of AI in recent years.

It’s a simple yet flexible language, which makes it ideal for AI programming. Python is also a high-level, interpreted language. This makes development and testing streamlined and efficient, which is perfect for the iterative process of AI projects.

On top of these benefits, the main advantage of Python for AI is its extensive libraries and frameworks. This includes PyTorch, Keras, TensorFlow, and more. These have pre-built functions and glasses, which allow developers to build complex algorithms very quickly.

As one of the most popular programming languages in the world, it has a vast, active community. This means that there are plenty of forums, tutorials, and third-party resources that developers can benefit from. This makes troubleshooting easier than it is with most other languages.

As there are various tools available, you can try out different options to figure out what works best. You can use them to automate processes, generate documents, create web crawlers, and more. If you want to learn AI programming, Python is a great place to start.

2. Java

Java is another very well-known programming language that can be ideal for AI projects. It’s typically best for large-scale, enterprise-level applications.

One of the most noteworthy benefits is that it’s platform independent. This means that a developer can create AI systems and easily transfer them across different environments.

When it comes to AI in particular, Java offers a growing field of natural language processing and neural networks. There are large programming libraries that offer a range of tools. This is ideal for the development of varying systems.

Java also offers impressive memory management and high-performance garbage collection mechanisms. This means it can handle large data sets efficiently, and these are common in AI.

As one of the most widespread programming languages in the world, integrating AI into existing systems is typically quite streamlined. Developers can build upon platforms that already exist rather than having to create everything from scratch.

Like Python, Java has a huge community that offers guidance and support. This is beneficial for developers and will make future advancements more likely.

Something that’s essential for AI languages is versatility, and Java excels here. It has already been implemented in a range of AI projects, and this will only continue in the future. It’s crucial in this ever-growing field where flexibility and scalability are key.

3. R

R is noteworthy for being an excellent choice when it comes to data visualization and statistical analysis. When data manipulation and analysis are crucial, this is one of the best AI coding languages available. In particular, it’s ideal for projects that have a primary focus on data, such as statistical computing and predictive modeling.

R has a comprehensive ecosystem with tools like dplyr and ggplot2 offering data manipulation and data visualization, respectively. These can help streamline data analysis, which is vital in AI. It makes it much easier for AI platforms to understand patterns and anomalies, resulting in higher quality I models.

Various sectors and industries use R. In research and academics, for example, it’s useful for things like machine learning, statistical computations, and the analysis of large data sets. It’s also a popular choice for IT companies as it’s very effective for business intelligence and data handling.

It helps with risk analysis, credit scoring, and financial modeling in the finance sector. E-commerce businesses can use it to provide a better user experience, sell products, and make targeted ads more efficient.codingtemple.com/software-engineering

It’s important to note that R doesn’t offer the same speed as Python or Java, which can be a crucial factor in some situations. With that in mind, its advanced statistical capabilities make it an excellent choice for specific AI projects. It has become one of the most favorable choices where large data sets and complex statistical operations are involved.

4. Prolog

Prolog (also known as Programming Logic) is a relatively new language. It was designed with AI in mind to meet specific needs. These include symbolic reasoning, pattern matching, problem-solving, and more.

Imperative languages follow a sequence of commands, but Prolog is different. It’s a declarative language, and it focuses on the relationship between facts and rules.

Prolog is mostly used for natural language processing, developing expert systems, and providing theorems. It stands out due to its backtracking and pattern-matching features. They make it a reliable choice when it comes to developing complex AI algorithms involving rule-based logical queries.

Beyond this, developers often use Prolog to retrieve information from large databases and build expert systems for specialized problem-solving.

It’s not as well-renowned, or widely used as Python or Java, but it can be more useful than either for certain applications. It has a unique approach to problem-solving and AI development. As such, when working on specific AI tasks (such as developing chatbots or rule-based systems) it can be one of the best choices available.

In many cases, more popular languages will be the better choice. With that in mind, it’s still incredibly useful to learn languages like Prolog. The more diverse your AI programming skills are, the more capable and flexible you’ll be.

5. C++

C++ is another programming language that has gained a lot of popularity over the years. Two of the most notable traits are its control over system resources and its high execution speeds. These make it a good choice if you’re trying to develop an AI application that relies on high performance.

It can run close to system hardware, which allows it to process AI algorithms with a high degree of efficiency. This is hugely beneficial for systems that have limited resources and real-time applications.

C++ is a common choice in machine learning and neural network development. It can be even more beneficial if latency is a concern.

For example, C++ powers the efficient matrix operations in TensorFlow. It’s able to do this by offering excellent speed and lower-level operations. Applications like this highlight the kind of valuable role that C++ can play in high-performance AI development.

An area in which C++ has become particularly popular is game development. AI is becoming more prominent here as it makes more complex and realistic environments possible. As C++ can handle complicated calculations at high speed, it offers superior performance in high-demand environments such as gaming and simulation.

Learning Programming Languages

In the world of AI development, knowledge is crucial. Having a solid understanding of these programming languages will help you stand out as a highly skilled developer.

Coding Temple offers various courses that cover software engineering, data analytics, quality assurance, and cyber security. With our coding boot camp, you can develop the skills you need to achieve your career goals.

We limit our enrollments to just 100 learners each month to ensure we can provide a personalized experience. This approach ensures every student gets a superior learning experience in a comprehensive learning environment.

On top of technical coaching and project-based learning, we even offer 1:1 career mentorship. For those who don’t know exactly where they want to take things in the future, we can offer detailed insights into various career paths that can help you decide.

We understand that everyone has different needs, so we offer various learning schedules. You can tackle things on a full-time or part-time basis, or we even provide flexible scheduling so you can move at your own pace.

Our software engineering course will teach you the skills you need to build software from scratch. This is an ideal path if you’re interested in becoming a software engineer, web developer, or front-end developer. You’ll learn about various coding languages and tools, and will even build your own portfolio throughout the duration of the course, giving you a great starting point.

Discovering the Best AI Tools for Your Career

AI development is all about understanding the latest technologies. It’s a constantly developing world, so it’s important to keep up with current developments if you want to achieve your career goals.

With Coding Temple, you learn about the best AI tools for your career. As many as 65% of our graduates are career changers, and they experience an average salary increase of $23,000. If this sounds like the right path for you, apply today.