Best programming languages for machine learning
Machine learning
Here in this article we are going to be talking about the best programming languages for machine learning that are mostly used. By yhe way before getting ahead let's have a quick overview of Machine Learning what actually it is. Machine learning is literally a subfield of artificial intelligence (AI) that allows you to develop the algorithms and models which enable computers to learn from and make predictions or decisions based on data. It is used for creating systems that should automatically work, learn and improve from experience without writing any kind of program. Literally using Machine learning features we build mathematical models or algorithms that can recognize patterns, make predictions, interpret, perform some of the specific task and uncover insights of data. Models are always used to train on the given data.
There can be used several programming languages for machine learning and the choice of language literally depends on the different factors that we want to develop our applications into, such as the specific machine learning tasks we want to perform and the familiarity with the programming language that we are to choose for, the availability of libraries and frameworks, the performance requirements for it and much more there can be.
Here is the list of Programming languages, you can go with.
- Python
- R
- Java
- C++
- Julia
- MATLAB
- Scala
- Go
1. Python:
Python is one of the most popular programming languages in the world and have made almost everything so simple ie- softwares, web applications, controls self-driving cars. Python is basically a general-purpose programming language, because it’s general purpose programming language we can a lot of thing using only one language but when it comes to Machine learning there are many python based frameworks and the libraries that are most widely used such as TensorFlow, PyTorch, Scikit-learn and Keras. These frameworks literally provide powerful tools for building and training machine learning models.
2. R:
R is a programming language commonly used for statistical computing and graphics. R programming language provides various statistical and graphical techniques, and it is highly extensible through user-created packages. Overall, R is widely used by statisticians, data scientists, researchers, and analysts due to its extensive statistical capabilities, flexible data visualization, and active community support But it's also most useful when we have to train and create machine learning based applications there are various packages and libraries such as Caret, MLR and RandomForest which are literally most used in the Machine learning field.
3. Java:
Java is most widely-used as a general-purpose programming language It was designed to be the independent platform. Java is one of the most popular programming languages due to its versatility and large extensive community. Java always focus over the regular updates and new versions of it, introducing new features and enhancements. If someone already has the familiarity with Java, well it's going to be best choice for him/her to go with Java to learn Machine learning because Java is evolving programming language and has a rich ecosystem of libraries and frameworks like Weka and Deeplearning4j, which provide a lot of tools for data preprocessing, feature selection, building neural networks and etcetra.
4. C++:
C++ is a robust and one of the most widely-used general-purpose programming language which was initially developed as an extension of the C programming language. It has wide range of applications we literally build with it such as system programming, game development, embedded systems, and high-performance computing. C++ is literally high-performance language often used in machine learning for various tasks that require efficiency and low-level control. Libraries which are most popular have the C++ APIs, TensorFlow, Caffe, and OpenCV are some of them which makes it suitable for implementing computationally intensive machine learning algorithms.
5. Julia:
Julia is a high-level high level programming language which was designed for numerical and scientific computing. It was developed for the ease of use and expressive power of languages like Python with the performance of low-level languages like C and Fortran. It has some of the built-in features for distributed computing and is gaining popularity in the machine learning community.
6. MATLAB:
MATLAB is high-level programming language mostly used in scientific and engineering applications which was developed by MathWorks. MATLAB word literally stands for "MATrix LABoratory," The name of language actually shows that it was initially designed for matrix manipulation and numerical computations. The reason MATLAB is so popular for mathematics is that it provides a various mathematical functions, operators what literally makes it efficient and at ease when it comes about performing complex computations involving matrices, vectors and arrays. MATLAB can even be used for machine learning. It offers comprehensive toolboxes for tasks like data analysis, signal processing, and neural networks.
7. Scala:
Scala is one of the programming language that runs on the Java Virtual Machine. It is often used in combination with Apache Spark, a distributed computing framework, for large-scale machine learning and data processing tasks.
8. Go:
Go, also known as Golang, is a language developed by Google that emphasizes simplicity and efficiency. Although it may not have as many specialized machine learning libraries as Python or R, Go is gaining popularity in the field, especially for building machine learning infrastructure and systems.
These are just a few examples of programming languages used in machine learning. Each language has its own strengths and weaknesses, so the choice depends on the specific requirements of the project and the preferences and expertise of the developer.
