Top 5 most popular programming languages used for machine learning in which Python tops the list, there's a few surprises on there.
Here we attached some link and references to make it easy for the concept of machine learning.
1. Python
Highly rated machine-learning programming language
To start python with machine learning first install some of libraries which is required in python machine learning:
NumPy: For numerical processing with Python.
PIL: A simple image processing library.
scikit-learn: Popular library for data mining and data analysis that implements a wide-range of machine-learning algorithms. Contains the machine learning algorithms we’ll cover today (we’ll need version 0.20+ which is why you see the --upgrade flag below).
Keras and TensorFlow: For deep learning. The CPU version of TensorFlow is fine for today’s example.
OpenCV: While we aren’t using OpenCV for this blog post, imutils depends upon it (next bullet). Because of this, you can simply use pip to install OpenCV, just bear in mind that you won’t have the full install of OpenCV and you can’t customize it.
imutils: My personal package of image processing/computer vision convenience functions
Machine Learning From Scratch: Bare bones but accessible implementations of machine-learning models and algorithms.
ChatterBot: A machine learning, conversational dialog engine for creating chat bots
2. R
Highly rated machine-learning programming language
Here some important libraries and references which make better for understand and learning of R:
These libraries mentioned here:
Dplyr: It is mainly used for data manipulation in R. Dplyr is actually built around these 5 functions. These functions make up the majority of the data manipulation you tend to do. You can work with local data frames as well as with remote database tables. You might need to:
Select certain columns of data.
Filter your data to select specific rows.
Arrange the rows of your data into an order.
Mutate your data frame to contain new columns.
Summarize chunks of you data in some way.
It also has functions like sample, group by and pipe.
Ggplot2: Ggplot2 is the one of the best library for data visualization in R. The ggplot2 library implements a “grammar of graphics” (Wilkinson, 2005). This approach gives us a coherent way to produce visualizations by expressing relationships between the attributes of data and their graphical representation. Ggplot2 has wide range of functions.
Esquisse: It allows you to draw bar graphs, curves, scatter plots, histograms, then export the graph or retrieve the code generating the graph. It’s awesome, isn’t it?
BioConductor: When you get into Data Science, you deal with different kinds of data. You may not know what sort of data you gotta deal with in future. If you are in health industry then trust me, you’ll find this very useful. I consider this library to be highly useful when you are working on genomic data. Bioconductor is an open source project that hosts a wide range of tools for analyzing biological data with R. To install Bioconductor Packages, you need to install biocmanager.
click here to install it- https://www.bioconductor.org/install/
Benchm-ml: A minimal benchmark for measuring scalability, speed and accuracy of commonly used open-source implementations of machine-learning algorithms.
Machine Learning in R : The framework provides code for supervised machine-learning methods like classification, regression and survival analysis, as well as unsupervised methods like clustering.
3. C++
Highly rated machine-learning programming language
tensorflow: Google's widely used machine-learning framework with APIs for a wide variety of languages.
Turi Create: A library that simplifies the development of custom machine-learning models for developers new to the field.
LightGBM: Microsoft's gradient boosting framework designed to help increase machine-learning model training speed and efficiency.
4. JavaScript
Highly rated machine-learning programming language
Flappy Learning: A program that learns how to play the infamous Flappy Bird game.
AI Blocks: A drag-and-drop WYSIWYG editor that aims to allow anyone to create Machine Learning models (also requires Python and tensorflow to be installed).
ml5.js: Aims to make machine learning usable by artists and non-technically minded students by offering access to machine learning algorithms and models in the browser.
5. Java
Highly rated machine-learning programming language
Smile: A fast and comprehensive system for carrying out machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala.
H20: An in-memory platform for distributed, scalable machine learning that works on existing big data infrastructure, on bare metal or on top of existing Hadoop or Spark clusters.
It offers APIs in Java (Restful API), Python, R and Scala. It has best of class algorithms for classification, Regression, Clustering etc. and seamlessly integrates with Apache Hadoop and Spark
EasyML: A general-purpose dataflow-based system designed to make it easier to apply machine learning algorithms to real-world tasks.
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