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10 Most Popular Machine Learning Software Tools In 2022

    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: scikit-learn

    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: Pytorch

    3) TensorFlow

    TensorFlow provides a JavaScript library that 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 for human pose estimation.

    Cons:

    • It is difficult to learn.

    Tool Cost/Plan Details: Free

    Official Website: Tensorflow

    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: Waikato-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: KNIME

    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: Colab

    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: Mahout – Apache

    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: Accord.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: Shogun

    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: Keras

    SOURCE :https://www.softwaretestinghelp.com/machine-learning-tools/