If you are a newbie in machine learning you may have thought that what programming language should I learn? Nowadays different people are working with different programming languages but among these many popular high-level programming languages, which one is the best for machine learning?
Machine Learning is one of the fastest-growing fields which has witnessed exponential growth in the technical world. There is no best language for machine learning it depends on what you want to build, To work in this field, you just need to learn only one particular programming language very well based on your own comfort, project requirements, and predilections. Just explore some of these mostly used languages and pick up one of your choices, you don’t need to take anyone’s recommendation.
1. Python
Python leads all the other languages with more than 60% of machine learning developers are using and prioritizing it for development because python is easy to learn. Scalable and open source. Python has many awesome visualization packages and useful core libraries like Numpy, scipy, pandas, matplotlib, seaborn, sklearn which really makes your work very easy and empower the machines to learn.
- Numpy: Numeric Python or Numpy is a Linear Algebra Library for Python with powerful data structures for efficient computation of multi-dimensional arrays and matrices.
- Pandas: It is the most popular Python library which provides highly optimized performance for data analysis.
- Matplotlib: It is a popular python plotting library used for creating basic graphs like line charts, bar charts, histograms, and many more.
- Seaborn: Provides a high-level interface for creating attractive graphs
- sci-kit Learn: It is used for data mining and data analysis which implements a wide-range of machine-learning algorithms like classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, k-means.
2. Java
This programming language is the “Jack of all the trade” and continues to dominate over in the ML industry also. Java provides many good environments like Weka, Knime, RapidMiner, Elka which used to perform machine learning tasks using graphical user interfaces.
- Weka: It is a free, portable library primarily used for data mining, data analysis, and predictive modelling and best used for machine learning algorithms. it is easy to use with the graphical interface and supports several standard data mining tasks, including data preprocessing, classification, clustering, and feature selection.
- JavaML: A Java API with simple and easy interfaces to implement the collection of machine learning and data mining algorithms in Java with clearly written and properly documented implementation of algorithms.
- Deeplearning4j: It is an innovative open-source distributed deep learning library that provides a computing framework with wide support for machine learning algorithms. This library is extremely useful for identifying patterns, sentiment, sound, and text and is designed especially for business environments.
- ELKI: It is a unique open-source data mining framework that mainly focused in the independent evaluation of data mining algorithms and data management and emphasizes in unsupervised methods. It also allows arbitrary data types, file formats, or distance or similarity measures.
3. C++
The superfast C++ programming language is also very popular in the field of machine learning. This powerful language gets supported by most of the machine learning platforms. If you have some good working knowledge using C++ then it is a pretty good idea to learn machine learning using C++. C++ is much efficient compare to most of programming languages. Many powerful libraries such as TensorFlow and Torch are implemented in the C++ programming language so machine learning and C++ is truly a great combination.
- TensorFlow: Google’s open-source TensorFlow is used to do numerical computations on any CPU or GPU using data flow graphs and make decisions with whatever information it gets.
- Torch: A open-source machine learning library which makes scientific and numerical operation easier by providing a large number of algorithms. it makes for easier and improved efficiency and speed.
- mlpack: A superfast, flexible machine learning library which provides fast and extensible implementations of cutting-edge machine learning algorithms using C++ classes which can be integrated into larger-scale machine learning solutions
4. R
R is a very popular programming language for statistical computing, analysis, and visualizations in machine learning. It is a perfect graphics-based language for exploring the statistical data via graph vastly used by data professionals at Facebook, Google, etc. Though R is highly preferable in bioengineering and biomedical statistic it is also popular in implementing machine learning like regression, classification, and decision tree formation.
- xgboost: this is used for implementing the gradient boosting framework and is popular for it’s performance and speed. It supports various objective functions like regression, classification, and ranking and is extensible so that you can define your own objectives easily.
- mlr: It is an extensible framework for classification, regression, and clustering problems and has easy extension mechanism through s3 inheritance.
- PARTY: this package is used for recursive partitioning. This package is used to build decision trees based on the Conditional Inference algorithm. This package is also extensive, which reduces the training time and bias.
- CARET: this package is developed to combine model training and prediction for several different algorithms for a given business problem and helps to choose the best machine learning algorithm.
5. Javascript
It is one of the most widely used, high-level, and dynamically typed languages which is flexible and multi-paradigm. Javascript is also so popular in ML that high-profile projects like Google’s Tensorflow.js are based on JavaScript. If you are a master of Javascript then literally you can do everything from full-stack to machine learning and NLP.
- Brain.js: It is a GPU accelerated, easy to integrate neural networks in JavaScript which is used with Node.js in the browser and provides multiple neural network implementations to train to do different things well. It is so simple to use that you do not need to know Neural Networks in detail to work with this.
- Tensorflow.js: It is a popular library for machine learning in JavaScript. You can build and train models directly in JavaScript using flexible APIs and almost any problems in Machine Learning can be solved using Tensorflow.js. You can also retrain the existing ML models using your own data.
- machinelearn.js: It is the savior of Javascript which is a replacement of python’s ScikitLearn library. It provides clustering, decomposition, feature extractions models, and utilities for supervised and unsupervised learning.
- face-api.js: A ready-to-use APIs that includes implementations of well-known Face Detection and Recognition models which is pre-trained with a wide variety of datasets. It gives you the flexibility to directly plug into any Node.js and browser environments. Being lightweight this library can be used on both mobile and web browsers with no issues.
Among these programming languages, Python remains the most popular in the field of ML. Though languages like JavaScript are the rising star and over time maybe it can reach the top. So it is a good recommendation to choose these languages.