There are dozens of programming languages that are used for data science, and the number of choices can make it difficult to choose which machine learning course to pick and which language to learn for a project. While it’s hard to decide which language is best for machine learning based on individual opinions, there’s a clear way to get real answers — the nature of the data and the expected outcome and performance. In this article, you’ll find some of the most popular programming languages for machine learning, along with some practical recommendations as to which ones are the best based on different use cases:
Language: Python
Python is a high-level programming language, and it is one of the most commonly preferred languages for general-purpose programming. Python is one of the most popular languages in the world, and it has been named the best language to learn in 2019.
Python can be used for both small and large projects. It is known as a general-purpose programming language, which means that it can be used to develop any type of application. Python has many libraries that are designed specifically for machine learning.
- Numpy – Numpy is a library for performing numerical operations on arrays and matrices. This library is used extensively in machine learning algorithms because it offers high-performance array computation. When it comes to scientific computing, Numpy is the most preferred package. Its name comes from “Numerical Python.”
- Pandas – Pandas is another library that provides data analysis and manipulation capabilities. This library makes it easy to manipulate data without having to write code from scratch every time you need to do something like sorting or filtering data from your dataset. This library provides
- Matplotlib – Matplotlib allows users to create figures and plots using Python code. Matplotlib is a Python 2D plotting library that allows you to create publication-quality figures in your applications.
- Seaborn – Seaborn is an extension of Matplotlib with more options for creating visualizations such as histograms, boxplots, etc., and also includes other useful functions such as faceting (changing colors or size of plotting symbols based on values). Seaborn simplifies the process of drawing attractive statistical graphics. It provides a full set of plotting methods, flexible scaling options, many built-in themes for customizing its appearance, and integration with other Python visualization libraries such as pandas and plotly.
- Sci-kit Learn – sci-kit-learn is a free software/open-source project maintained by NumFOCUS that provides implementations of several machine learning algorithms, including clustering, classification and regression, dimensionality reduction as well as model evaluation metrics, among others.
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Language: R
R is a statistical programming language that is used for data analysis and statistical modeling. It has been around since the 1980s and has become one of the most popular languages for machine learning due to its wide range of packages and libraries.
R can be used for both supervised and unsupervised learning tasks, including deep learning. There are many different packages available that have been developed by researchers at universities and companies like Microsoft, Google, Facebook, etc. Some of these include:
- xgboost – This is an open-source library that provides fast-boosting algorithms. It is used to train decision tree models (DTMs) with gradient boosting trees (GBTs). The library was originally developed by Tianqi Chen while he was working at Microsoft Research Asia. It was released as an open-source project in 2012 with the goal of improving the performance and ease of use of existing boosting libraries. The library includes support for parallel computing, which allows users to train models on large datasets quickly.
- mlr – This package contains functions for fitting generalized linear mixed models (GLMM), generalized additive mixed models (GAMM), or penalized regression models (e.g., lasso and ridge) via maximum likelihood, via restricted maximum likelihood, or via Bayesian methods. GAMMs can be fitted to both fixed and random effects structures simultaneously, but only the latter is implemented. The package also provides a function for fitting nonparametric regression models to data from a single sample using nonparametric smoothing splines.
- PARTY- This package provides tools for creating feature subspaces using principal component analysis (PCA) algorithms such as linear discriminant analysis. It also supports PCA-based dimensionality reduction.
- CARET- The CARET library provides a wide range of supervised and unsupervised machine learning methods that are implemented in R. These include classification, regression, clustering, and dimensionality reduction techniques.
Language: JAVA
Java is another language that you can use for machine learning. It is a general-purpose programming language that was developed by Sun Microsystem in 1991. Java is also a platform-independent language. This means that you can write code in Java and then run it on different platforms like Windows, Linux, or macOS without any problem.
Some of the Java libraries which can be used for machine learning purposes are:
1. WEKA – This is an open-source collection of algorithms for data mining and data analysis. You’ll find tools for clustering, classification, regression, association rules mining, visualization, and other techniques.
2. JavaML – This is a library that provides tools for implementing different types of machine learning algorithms based on existing Java libraries such as Weka or GAMA. The advantage of JavaML is that it allows you to combine different algorithms into one pipeline and use them simultaneously.
3. Deeplearning4j – Deeplearning4j (DL4J) is an open-source deep reinforcement learning library written in Java and Scala. It relies on distributed GPUs to scale up training from hours to days or weeks, depending on problem size and data volume. The library has been used in many industrial applications such as computer vision and autonomous driving systems.
4. ELKI- ELKI is a general-purpose machine learning software package that is designed for high performance, modularity, and flexibility. It aims to provide an easy-to-use framework that allows users to quickly implement their own algorithms based on existing implementations of state-of-the-art algorithms (e.g., SVM).
Language: C++
C++ programming language was developed by Bjarne Stroustrup at Bell Labs in 1979. The language has evolved over time to support object-oriented features and multiple inheritances, among other features. C++ is still used today for many programs, including console applications, graphics software, and games. However, one of the most important uses of C++ is in computer science education, where it is used as an introductory programming language. C++ has been used to implement nearly every popular operating system, including Windows, Linux, macOS and Android.
The use of C++ in machine learning started with its use as a host language for Tensorflow and Torch libraries which are compiled into native code on each platform they run on (e.g., x86_64). This allows these libraries to be used on all platforms without having to port them manually each time you want to use them on a new one.
- Tensorflow- The primary open-source library for C++ is TensorFlow which is developed by Google. This library provides parallel computation on multi-core CPUs and GPUs. It also supports complex mathematical functions like linear algebra, calculus, matrix operations, etc. The main aim of this library is to provide an easy way to design neural networks and perform large-scale numerical computations. You can also use this library to develop distributed systems that are capable of running on multiple machines.
These were some of the popular languages used for machine learning. If you want to dig deeper, you can take a machine learning course online and learn about these languages.