If you are a newbie in machine learning you may have thought that what programming language should I learn? Nowadays different peoples 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 an 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.
Python leads all the other languages with more than 60% of machine learning developers are using and prioritising 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 .
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 which 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 which 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 which mainly focused in the independent evaluation of data mining algorithms and data management and emphasise in the unsupervised methods. It also allows arbitrary data types, file formats, or distance or similarity measures.
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 the programming languages. Many powerful libraries such as TensorFlow and Torch are implemented in C++ programming language so machine learning and C++ is truly a great combination.
- TensoFlow: 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
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 the biomedical statistic it is also popular on 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 Conditional Inference algorithm. This package is also extensively, 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.
- face-api.js: A ready-to-use APIs which 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.