List of 15 Most Popular Machine Learning Software Tools In 2022

(1) Scikit-learn

Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities.

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(2) PyTorch

With TorchScript, PyTorch provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.

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(3) TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

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(4) Weka 3: Machine Learning Software in Java

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature.

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(5) KNIME

At KNIME, we build software to create and productionize data science using one easy and intuitive environment, enabling every stakeholder in the data science process to focus on what they do best.

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(6) Colab

Whether you’re a student, a data scientist or an AI researcher, Colab can make your work easier. The document you are reading is not a static web page, but an interactive environment called a Colab notebook that lets you write and execute code.

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(7) Apache Mahout

Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily on linear algebra.

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(8) Accord.Net

The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the details.

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(9) Shogun Machine Learning

Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems.

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(10) Keras.io

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.

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(11) Rapid Miner

RapidMiner’s data science platform delivers transformational business impact for over 40,000+ organizations in every industry to drive revenue, reduce costs, and avoid risks.

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(12) Amazon Machine Learning (AML)

Make accurate predictions, get deeper insights from your data, reduce operational overhead, and improve customer experience with AWS machine learning (ML). AWS helps you at every stage of your ML adoption journey with the most comprehensive set of artificial intelligence (AI) and ML services, infrastructure, and implementation resources.

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(13) Oryx 2

Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning. It is a framework for building applications, but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering.

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(14) Apache Spark MLlib

MLlib fits into Spark’s APIs and interoperates with NumPy in Python (as of Spark 0.9) and R libraries (as of Spark 1.5). You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.

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(15) Core ML Machine Learning Algorithm

Core ML applies a machine learning algorithm to a set of training data to create a model. You use a model to make predictions based on new input data. Models can accomplish a wide variety of tasks that would be difficult or impractical to write in code. For example, you can train a model to categorize photos, or detect specific objects within a photo directly from its pixels.

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