This guide is designed for beginners. Discover the most popular languages and their applications in AI. Learn about main packages, roles they play, and the resources you can use to get started.
Many beginners want to be involved in the AI revolution. The first step is to choose the right programming languages.
In this post, I will introduce you to 10 of the most popular AI programming languages. I will also discuss how they are used by AI, highlight the top packages and libraries of each language, discuss the AI course, and provide resources that can help you get started.
AI programming languages are used to create applications that simulate human intellect. These languages allow the creation of algorithms that can learn from data and make decisions. Each language has its own libraries and frameworks that help to develop tasks like speech recognition, data interpreting, and autonomous driving.
It is important to choose a programming language that offers libraries for machine learning, data analysis, and other tasks. Python, for example, is known for libraries such as TensorFlow, PyTorch, and R, which are used for statistical analysis.
AI programming languages are the foundation of intelligent systems. They combine code with the ability to perform complex AI-specific tasks.
Here is my list of the top 10 AI programming languages in 2024. Python is the clear leader of this list.
1. Python
Python is the most user-friendly and popular programming language for AI. Python’s syntax is clear, and it has a wide variety of libraries and packages.
TensorFlow, PyTorch, and other leading platforms help us create powerful AI models. Libraries such as Scikit-Learn are helpful for machine learning tasks. Python is the best choice for AI development because it combines advanced features and convenience.
- What is the use of Python in AI? Python’s comprehensive range of AI applications is used widely.
- Top Python AI libraries and packages –
- NumPy can easily handle complex mathematical structures
- Pandas provides data structures for users, such as DataFrames, to analyze and manipulate data.
- Matplotlib is a tool for creating animated and interactive graphs.
- Scikit-learn offers a variety of tools and algorithms to support machine learning.
- TensorFlow – Google’s brainchild is a solid foundation for building and implementing neural networks.
Python roles for AI
Python is essential for AI professionals. It provides useful toolsfor a wide range of roles, from computer vision specialists to machine learning engineers. You can learn more about improving your skills by taking our courses on how to become a data scientist, how to become a data engineer, and the machine-learning engineer track. Here are some AI jobs that utilize Python –
- Machine learning engineers are responsible for developing and implementing machine-learning models using Python libraries, such as TensorFlow, PyTorch and Scikit-Learn.
- Data scientists use Python to perform tasks such as data cleaning, manipulation, and visualization using libraries such as Pandas and Matplotlib.
- Engineers in natural language processing build and deploy systems that can understand and process human language for tasks such as machine Translation.
- Computer vision engineers enable machines to analyze visuals efficiently. TensorFlow libraries provide functionality for video processing tasks.
- Researchers use Python for prototyping and implementing new AI algorithms. Python’s library and packages are used in a wide range of science fields.
Python Resources to Get Started
Python is easy to use, which makes it popular among beginners. You can start by choosing from our tutorials, blog posts, or courses.
- Learn Python
- Introduction Python
- Intermediate Python
- Data manipulation using pandas
- Machine Learning Fundamentals with Python
2. R programming
R is a program for statistical analysis that includes data cleaning, visualization, interpretation, machine learning, and statistical modeling.
- How can R be used in AI? R is a powerful statistical tool, and the data visualization tools it offers have made it an attractive option for developing models and prototypes of machine learning algorithms.
- Top R AI libraries and packages –
- Tidyverse is not one library. It’s a collection of packages that work together seamlessly on data science tasks.
- caret is an all-in-one solution for machine learning in R. The interface provides a unified user interface for different machine learning algorithms. Users can easily train, tune and evaluate models.
- XGBoost is a powerful tool for gradient boosting. This popular machine-learning method is known for its accuracy.
- randomForest As its name implies, randomForest implements a random forest algorithm. This is a popular ensemble-learning technique that combines predictions from multiple decision trees.
- IML – This framework provides a framework for building statistical models in R.
Roles that AI uses R
R is essential to specific AI niches, especially where analysis and statistical expertise are crucial. This versatile language is used in a variety of AI jobs, including data science and bioinformatics. Its powerful statistical and visualization tools are utilized in each. Here are some AI jobs that utilize R –
- Data scientist (specialist in R) – While Python is widely used for data science, R continues to be a key player when it comes to tasks that require statistical analysis and model building.
- AI researcher (focus on statistical modeling). Researchers prefer R due to its computing and visualization capabilities. Researchers can use R to create, test, and evaluate models to help AI algorithms progress.
- Scientists in bioinformatics – They use R to perform tasks such as gene expression analysis and variation discovery. These are crucial for AI-driven applications within genomics and Bioinformatics.
- Econometrician – These are economists who use AI to develop models that predict trends, assess risks and forecast market behavior.
- Social data scientists – they use R to perform social network and textual analysis tasks.
Start with Top R Resources
R offers powerful statistical analysis, clear visualizations of data, and an easy-to-use environment for creating machine-learning models. Browse our courses, tutorials and blog posts. You can start by exploring the following options –
- Introduction of R
- Intermediate R
- Introduction of Tidyverse
- Exploratory data analysis in R
- Machine Learning Scientist with R
3. Julia
Julia is a powerful data analysis tool that can handle complex calculations and has a fast processing speed.
- How can Julia be used in AI? Julia’s speed makes it the ideal choice for advanced machine-learning models.
- Top Julia AI libraries and packages –
- It’s a popular framework for learning that offers an intuitive way to build and fine-tune neural networks.
- jl is the ideal tool for data visualization tasks.
- jl provides a robust framework for solving differential problems of all types.
- jl is Julia’s standard statistical library.
- jl is a tool that helps in data visualization through the understanding of complex data and model behavior.
Julia is used in AI roles
Julia’s high-performance AI capabilities are becoming increasingly well-known. This dynamic language has been used in a variety of AI specializations, from robotics to financial analysis, to enhance the deployment of innovative solutions. Here are some AI jobs that use Julia.
- AI Engineer – Julia is used to build and train robust models.
- AI researcher (algorithmic Optimization) – They create prototypes, evaluate and assess their algorithms’ effectiveness before converting them into commonly used languages.
- Machine learning engineer – They use Julia’s libraries, such as Flux.jl to create AI solutions in industries such as finance.
- Robotics Engineer (AI-driven Robotics) – Julia’s ability to handle real-time information and integrate it into robotics frameworks such as ROS (Robot Operating System), makes it a useful tool for robotics engineering professionals working on AI driven robots.
- Financial analyst (AI trading) – Julia is a strong asset to analysts who are developing AI-based trading models and algorithms.
Start with these Julia resources
Julia’s expanding set of deep learning tools, combined with its solid math foundation, makes it a great tool for creating AI models. Choose one of the following courses to start –
- Introduction of Julia
- Intermediate Julia
- Data manipulation in Julia
- Introduction Data Visualization in Julia
- Julia Machine Learning Introduction
4. Scala
Scala is a leader in AI and machine learning due to its integration with Apache Spark and scalability. Scala’s compatibility with Java and functional programming abilities make it a powerful tool for developing AI systems.
- How can Scala be used in AI? Scala excels in handling data, functional programming, and managing large-scale AI apps. This is especially true when used with Apache Spark.
- Top Scala AI libraries and packages –
- Apache Spark – This tool provides distributed computing, data stream, and machine-learning features, making it an essential tool for creating AI applications in Scala.
- SparkMLlib – This library offers a variety of machine-learning algorithms to perform tasks like classification, regression and clustering. It also allows you to build recommendation systems.
- Breeze – This includes linear algebra operations, statistical functions, and optimization methods. It provides a foundation for building AI models in Scala.
- Deeplearning4j – Although DL4J’s primary platform is Java, it offers a Scala-based interface for constructing learning models and educating them. It integrates seamlessly with Apache Spark and accommodates network structures.
- Scopt – Although it is not classified as an AI library (it’s a command-line interface), it allows developers to create user command-line interfaces to manage and set up their AI models. This simplifies their deployment and usage processes.
Scala is used for AI roles
Scala is used where complex algorithms and large-scale processing of data are the norm. This language is used for a wide range of AI roles, and it offers unique advantages when it comes to big data integration and systems engineering. Scala is used in the following job roles –
- Engineers who specialize in machine learning build and deploy large-scale machines and systems.
- Data scientists – They use this for data cleaning, manipulation, and custom machine learning pipelines.
- Spark developer – Apache Spark, a framework for big data processing, is built in Scala. Spark developers will likely be employed by a company that uses Spark to perform AI tasks.
- AI researcher – AI researchers use Scala to prototype new algorithms and build specialized tools because of its flexibility.
- Scala backend developers (for AI), they use Scala for the development and maintenance of server-side functionality.
Scala Resources to Get Started
Scala is a great tool for creating AI solutions and managing large datasets. Scala’s focus on programming, and its seamless connection with Apache Spark makes it a valuable tool for developing AI projects and working with large datasets.
5. Java
Java is used to create sophisticated AI software. It is well-known for its ability to build adaptable systems that are compatible with different computer platforms, and integrate AI libraries to implement machine-learning algorithms.
- How can Java be used in AI? Java is used to create large, cross-platform AI applications.
- Top Java AI libraries and packages –
- WEKA is a collection of machine-learning algorithms.
- DeepJAVA is a Java tool for creating and training deep neural networks.
- H2O – The model training is done using distributed computing.
- Deeplearning4j is a deep-learning library that supports models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and others.
- Smile is a machine learning model development tool that focuses on efficiency and clarity.
Java-based AI roles
Java is a cornerstone of AI development. It offers robust frameworks and libraries that assist with the integration of AI into different platforms, such as big data systems or mobile applications. Here are some AI jobs that utilize Java –
- Big data engineer – These engineers use Java frameworks to build and manage AI pipelines for large volumes of data. They also provide tools such as machine-learning algorithms and distributed processing.
- Java libraries like Deeplearning4j or H2O are used by enterprise AI developers to integrate AI models in existing Java enterprise systems. Businesses can now take advantage of AI capabilities without having to invest in complex infrastructure.
- Android AI developer – These developers use tools such as TensorFlow Light, an optimized version of TensorFlow Mobile, to integrate AI-driven functionality into Android applications.
- AI Infrastructure Engineer (Java Backend for AI). They create APIs to expose AI models and features to applications. Java-based pipelines and monitoring tools can be used to make sure that AI models are running smoothly in production environments.
- Software Engineer (AI Integration with Java Systems) – They integrate AI libraries or models like scikit-learn into Java-based Java applications using Java APIs. This allows them to utilize AI capabilities.
To begin your journey into AI, you will need to master key skills like basic mathematics (like calculus and linear algebra) and statistics. These are the foundations for understanding AI algorithms and operations.
Python is a great language to learn because of its simplicity, and AI can use a lot of resources. Data manipulation skills and an understanding of data structures is essential to implement AI solutions.
You can build up your AI expertise by continuously updating these skills through targeted learning and practical projects.
Check out How to Learn AI From Scratch for a complete guide that includes detailed learning plans, resources, and more.
Starting your AI study can be an exciting and rewarding journey. Here’s how you can begin your AI education using the listed online course of artificial intelligence.
- Coursera: Introduction to TensorFlow (for Artificial Intelligence and Machine Learning)
- Artificial Intelligence A-Z: Build 5 AI (including ChatGPT).
- University of Texas, Great learning AI Program
- Artificial Intelligence Engineer Certification Process by Artificial Intelligence Board of America
AI programming can be a very exciting experience. Start experimenting with your chosen language and learning as you go.
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