Chat GPT, machine learning, AI are in everyone mind these days. But these neural networks have hundreds of people coding them. The top 10 programming languages for machine learning, AI, and deep learning are:
- Python: Python is the most popular programming language for machine learning and AI due to its simplicity, ease of use, and vast array of libraries and frameworks such as TensorFlow, Keras, PyTorch, and Scikit-learn.
- R: R is another popular language for machine learning and statistical analysis due to its extensive libraries and tools for data visualization, analysis, and modeling.
- Java: Java is widely used in enterprise-level machine learning applications, particularly for building complex AI systems such as chatbots and natural language processing (NLP) systems.
- C++: C++ is a powerful language for high-performance computing and is widely used for building deep learning frameworks such as TensorFlow and Caffe.
- MATLAB: MATLAB is a programming language widely used in academia and industry for machine learning, particularly in signal processing, image analysis, and computer vision.
- Julia: Julia is a relatively new programming language designed specifically for scientific computing and high-performance numerical analysis, making it ideal for machine learning and AI applications.
- Lisp: Lisp is a functional programming language with a rich history in AI and machine learning applications due to its ability to handle symbolic expressions, making it ideal for natural language processing and expert systems.
- Scala: Scala is a powerful language that runs on the Java Virtual Machine (JVM) and is often used for building large-scale machine learning applications due to its ability to handle big data processing.
- Go: Go is a relatively new programming language that is growing in popularity for building machine learning applications, particularly for its simplicity and ability to handle concurrency.
These languages are popular because they offer various libraries, frameworks, and tools that make it easy to develop machine learning models and AI applications. Additionally, these languages are actively maintained and have large communities, making it easy to get support and find resources.