MACHINE LEARNING TOOLS FOR RESEARCH WORK

 Machine learning is an astonishing technology. It is also a very overwhelming one to execute in the correct way. It is also used to build a machine that behaves like a human being to a great extent. Mastering machine learning tools will help to deal with the data, train the models, discover new methods, and create new algorithms. Machine learning comes with an extensive collection of ML tools, platforms, and software.

The following list shows the machine learning tools that are widely used by experts.

1) Scikit-learn

Scikit-learn is for machine learning development in python. It provides a library for the Python programming language.

Features:

It helps in data mining and data analysis.

It provides models and algorithms for Classification, Regression, Clustering, Dimensional reduction, Model selection, and Pre-processing.

Pros:

Easily understandable documentation is provided.

Parameters for any specific algorithm can be changed while calling objects.

Tool Cost/Plan Details: Free.

Official Website: https://scikit-learn.org/stable/

2) PyTorch

PyTorch is a Torch based, Python machine learning library. The torch is a Lua based computing framework, scripting language, and machine learning library.

Features:

It helps in building neural networks through Autograd Module.

It provides a variety of optimization algorithms for building neural networks.

PyTorch can be used on cloud platforms.

It provides distributed training, various tools, and libraries.

Pros:

It helps in creating computational graphs.

Ease of use because of the hybrid front-end.

Tool Cost/Plan Details: Free

Official Website: https://pytorch.org/

3) TensorFlow

TensorFlow provides a JavaScript library which helps in machine learning. APIs will help you to build and train the models.

Features:

Helps in training and building your models.

You can run your existing models with the help of TensorFlow.js which is a model converter.

It helps in the neural network.

Pros:

You can use it in two ways, i.e. by script tags or by installing through NPM.

It can even help human pose estimation.

Cons:

It is difficult to learn.

Tool Cost/Plan Details: Free

Official Website: https://www.tensorflow.org/

4) Weka

These machine learning algorithms help in data mining.

Features:

Data preparation

Classification

Regression

Clustering

Visualization and

Association rules mining.

Pros:

Provides online courses for training.

Easy to understand algorithms.

It is good for students as well.

Cons:

Not much documentation and online support are available.

Tool Cost/Plan Details: Free

Official Website: https://www.cs.waikato.ac.nz/ml/weka/

5) KNIME

KNIME is a tool for data analytics, reporting and integration platform. Using the data pipelining concept, it combines different components for machine learning and data mining.

Features:

It can integrate the code of programming languages like C, C++, R, Python, Java, JavaScript etc.

It can be used for business intelligence, financial data analysis, and CRM.

Pros:

It can work as a SAS alternative.

It is easy to deploy and install.

Easy to learn.

Cons:

Difficult to build complicated models.

Limited visualization and exporting capabilities.

Tool Cost/Plan Details: Free

Official website: https://www.knime.com/

6) Colab

Google Colab is a cloud service which supports Python. It will help you in building the machine learning applications using the libraries of PyTorch, Keras, TensorFlow, and OpenCV.

Features:

It helps in machine learning education.

Assists in machine learning research.

Pros:

You can use it from your google drive.

Tool Cost/Plan Details: Free

Official Website: https://colab.research.google.com/notebooks/welcome.ipynb#recent=true

7) Apache Mahout

Apache Mahout helps mathematicians, statisticians, and data scientists for executing their algorithms.

Features:

It provides algorithms for Pre-processors, Regression, Clustering, Recommenders, and Distributed Linear Algebra.

Java libraries are included for common math operations.

It follows Distributed linear algebra framework.

Pros:

It works for large data sets.

Simple

Extensible

Cons:

Needs more helpful documentation.

Some algorithms are missing.

Tool Cost/Plan Details: Free

Official Website: https://mahout.apache.org/

8) Accord.Net

Accord.Net provides machine learning libraries for image and audio processing.

Features:

It provides algorithms for:

Numerical linear algebra.

Numerical optimization

Statistics

Artificial Neural networks.

Image, audio, & signal processing.

It also provides support for graph plotting & visualization libraries.

Pros:

Libraries are made available from the source code and also through executable installer & NuGet package manager.

Cons:

It supports only. Net supported languages.

Tool Cost/Plan Details: Free

Official Website: http://accord-framework.net/

9) Shogun

Shogun provides various algorithms and data structures for machine learning. These machine learning libraries are used for research and education.

Features:

It provides support vector machines for regression and classification.

It helps in implementing Hidden Markov models.

It offers support for many languages like – Python, Octave, R, Ruby, Java, Scala, and Lua.

Pros:

It can process large data-sets.

Easy to use.

Provides good customer support.

Offers good features and functionalities.

Tool Cost/Plan Details: Free

Official Website: https://www.shogun-toolbox.org/

10) Keras.io

Keras is an API for neural networks. It helps in doing quick research and is written in Python.

Features:

It can be used for easy and fast prototyping.

It supports convolution networks.

It assists recurrent networks.

It supports a combination of two networks.

It can be run on the CPU and GPU.

Pros:

User-friendly

Modular

Extensible

Cons:

In order to use Keras, you must need TensorFlow, Theano, or CNTK.

Tool Cost/Plan Details: Free

Official Website: https://keras.io/

11) Rapid Miner

Rapid Miner provides a platform for machine learning, deep learning, data preparation, text mining, and predictive analytics. It can be used for research, education and application development.

Features:

Through GUI, it helps in designing and implementing analytical workflows.

It helps with data preparation.

Result Visualization.

Model validation and optimization.

 Pros:

Extensible through plugins.

Easy to use.

No programming skills are required.

Cons:

The tool is costly.

Tool Cost/Plan Details:

It has four plans:

Free plan

Small: $2500 per year.

Medium: $5000 per year.

Large: $10000 per year.

Official Website: https://rapidminer.com/

COMPARISON CHART

S.No	Tools	Platform	Cost	Written in language	Algorithms or Features
1	Scikit Learn	Linux, Mac OS, Windows	Free	Python, Cython, C, C++	Classification Regression Clustering Preprocessing Model Selection Dimensionality reduction.
2	PyTorch	Linux, Mac OS, Windows	Free	Python, C++, CUDA	Autograd Module Optim Module nn Module
3	TensorFlow	Linux, Mac OS, Windows	Free	Python, C++, CUDA	Provides a library for dataflow programming.
4	Weka	Linux, Mac OS, Windows	Free	Java	Data preparation Classification Regression Clustering Visualization Association rules mining
5	KNIME	Linux, Mac OS, Windows	Free	Java	Can work with large data volume. Supports text mining & image mining through plugins
6	Colab	Cloud Service	Free	-	Supports libraries of PyTorch, Keras, TensorFlow, and OpenCV
7	Apache Mahout	Cross-platform	Free	Java Scala	Preprocessors Regression Clustering Recommenders Distributed Linear Algebra.
8	Accors.Net	Cross-platform	Free	C#	Classification Regression Distribution Clustering Hypothesis Tests & Kernel Methods Image, Audio & Signal. & Vision
9	Shogun	Windows Linux UNIX Mac OS	Free	C++	Regression Classification Clustering Support vector machines. Dimensionality reduction Online learning etc.
10	Keras.io	Cross-platform	Free	Python	API for neural networks
11	Rapid Miner	Cross-platform	Free plan Small: $2500 per year. Medium: $5000 per year. Large: $10000 per year.	Java	Data loading & Transformation Data preprocessing & visualization.

Conclusion

The selection of the tool depends on your requirement for the algorithm, your expertise level, and the price of the tool. Machine learning library should be easy to use.

Most of these libraries are free except Rapid Miner. TensorFlow is more popular in machine learning, but it has a learning curve. Scikit-learn and PyTorch are also popular tools for machine learning and both support Python programming language. Keras.io and TensorFlow is good for neural networks.

  Source: Software Testing Help