Best Machine Learning Software in 2024

8.8

★★★★★

Torch

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.

Torch tool easy to use fast, automatic and parallel. - Pedro M.

Ease of use

8.9

Support

8.8

Ease of Setup

9.0

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8.2

★★★★★

MLlib is Spark's machine learning (ML) library that make practical machine learning scalable and easy it provides ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering, feature extraction, transformation, dimensionality reduction, and selection, tools for constructing, evaluating, and tuning ML Pipelines, saving and load algorithms, models, and Pipelines and linear algebra, ...

Apache Spark - MLib review - Chetan S.

Ease of use

8.8

Support

7.9

Ease of Setup

9.0

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9.0

★★★★★

python-recsys

python-recsys is a python library for implementing a recommender system.

Very helpful and easy to use - Caroline M.

Ease of use

8.3

Support

9.3

Ease of Setup

7.1

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8.6

★★★★★

Amazon Personalize

Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications.

An easy and user friendly app to use - VICTOR N.

Ease of use

9.3

Support

8.0

Ease of Setup

0.0

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7.6

★★★★★

Amazon Forecast

Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts.

One of the best forcast algo at work behind this! - Naman G.

Ease of use

8.7

Support

7.0

Ease of Setup

0.0

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9.0

★★★★★

Weka

Weka is a machine learning algorithms for data mining tasks that can either be applied directly to a dataset or called from own Java code, it contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization and well-suited for developing new machine learning schemes.

Ideal for classification - User in Management Consulting

Ease of use

8.2

Support

9.2

Ease of Setup

8.3

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8.8

★★★★★

XGBoost

XGBoost is an optimized distributed gradient boosting library that is efficient, flexible and portable, it implements machine learning algorithms under the Gradient Boosting framework and provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

The greatest boosting algorithm that existed so far - Meliksah T.

Ease of use

8.9

Support

8.8

Ease of Setup

8.1

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7.8

★★★★★

Microsoft Machine Learning Server

Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features algorithmic ...

Microsoft machine learning server is user friendly and efficient! - User in Real Estate

Ease of use

9.0

Support

6.9

Ease of Setup

0.0

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8.6

★★★★★

Mahout

Apache Mahout is a software that build an environment for quickly creating scalable performant machine learning applications, it provides three major features: A simple and extensible programming environment and framework for building scalable algorithms, A wide variety of premade algorithms for Scala + Apache Spark, H2O, Apache Flink and Samsara, a vector math experimentation environment with R-like syntax which works at scale

Great system, super efficient! - User in Financial Services

Ease of use

7.4

Support

8.6

Ease of Setup

0.0

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8.2

★★★★★

GoLearn

GoLearn is a 'batteries included' machine learning library for Go that implements the scikit-learn interface of Fit/Predict, to easily swap out estimators for trial and error it includes helper functions for data, like cross validation, and train and test splitting.

Very easy to use - M.Sc. Jonathan S.

Ease of use

9.5

Support

10.0

Ease of Setup

0.0

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9.2

★★★★★

Crab

Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set of components from which user can construct a customized recommender system from a set of algorithms and be usable in various contexts: ** science and engineering ** .

Recommender System Builder For Everyone - Haru K.

Ease of use

8.7

Support

9.6

Ease of Setup

8.7

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8.8

★★★★★

Xilinx Machine Learning

The Xilinx ML Suite enables developers to optimize and deploy accelerated ML inference. It provides support for many common machine learning frameworks such as Caffe, MxNet and Tensorflow as well as Python and RESTful APIs.

Great Machine learning application - naresh r.

Ease of use

8.6

Support

8.9

Ease of Setup

8.5

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8.0

★★★★★

Patern Recognition and Machine Learning Toolbox

Pattern Recognition and Machine Learning is a Matlab implementation of the algorithms.

A complete toolbox for ML application - Meryem S.

Ease of use

7.3

Support

8.1

Ease of Setup

0.0

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9.0

★★★★★

ml.js

ml.js is a machine learning and numeric analysis tools in javascript for node.js and browser.

Machine Learning with JavaScript - Varsha S.

Ease of use

Support

Ease of Setup

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9.0

★★★★★

Feature Forge

Feature Forge is a set of tools for creating and testing machine learning features, with a scikit-learn compatible API

Feature Forge - Deepika V.

Ease of use

Support

Ease of Setup

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9.0

★★★★★

Spearmint

Spearmint is a software package to perform Bayesian optimization that automatically run experiments (thus the code name spearmint) in a manner that iteratively adjusts a number of parameters so as to minimize some objective in as few runs as possible.

Easy Optimization - G2 User in Entertainment

Ease of use

Support

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9.0

★★★★★

Cortex Fabric

Cortex Fabric automates composition, orchestration and delivery of intelligent, personalized and explainable predictions, processes, and applications with built-in visibility and control for implementing trust as a service in your AI platform and cloud of choice.

It was really good - Atif F.

Ease of use

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9.0

★★★★★

ASAPP

ASAPP's AI-Native platform was designed explicitly to make people better. Most customers still prefer human interaction to engaging with a bot – but these interactions are complex. We provide sophisticated machine learning to optimize these interactions – and drive real productivity gains for your business.

ASAPP Works well for Chat support to customers - User in Internet

Ease of use

Support

Ease of Setup

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9.0

★★★★★

AIToolbox

AIToolbox is a toolbox of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians, Logistic Regression

Easy to use - G2 User in Outsourcing/Offshoring

Ease of use

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8.0

★★★★★

Apache SystemML

Apache SystemML is a machine learning platform optimal for big data that provides an optimal workplace for machine learning using big data, it can be run on top of Apache Spark, where it automatically scales your data, line by line, determining whether your code should be run on the driver or an Apache Spark cluster.

My feedback on Apache SystemML - G2 User in Information Technology and Services

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8.0

★★★★★

OpenAI Gym

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms that supports teaching agents everything from walking to playing games like Pong or Go.

OpenAI Gym - Kevin T.

Ease of use

Support

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8.0

★★★★★

Recommender

Recommender is a tool that analyzes the the feedback of some users (implicit and explicit) and their preferences for some items to learns patterns and predicts the most suitable products for a particular user.

Nice - G2 User in Hospital & Health Care

Ease of use

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8.0

★★★★★

Ganitha

Ganitha is an open-source library (derived from the Sanskrit word for mathematics, or science of computation) is a Scalding library with a focus on machine-learning and statistical analysis.

Amazing! Amazing! - G2 User in Computer Software

Ease of use

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8.0

★★★★★

metric-learn

metric-learn is the sub-field of machine learning dedicated to automatically constructing optimal distance metrics.

Machine Learning library for Python - Kevin T.

Ease of use

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8.0

★★★★★

MLBase.jl

MLBase.jl is a swiss knife for machine learning that does not implement specific machine learning algorithms, instead, it provides a collection of useful tools to support machine learning programs, including: Data manipulation & preprocessing, Score-based classification, Performance evaluation (e.g. evaluating ROC), Cross validation and Model tuning (i.e. search best settings of parameters).

MLB - maddy L.

Ease of use

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Ease of Setup

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8.0

★★★★★

SAS Factory Miner

SAS FACTORY MINER is a softwarethat automatically build and retrain hundreds of predictive models across multiple segments and pick the best model for each segment to reveal new opportunities, expose hidden risks, and fuel smarter, well-timed decisions.

SAS for Advanced Analytics and Data Mining - G2 User in Retail

Ease of use

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Ease of Setup

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8.0

★★★★★

AForge.MachineLearning

AForge.MachineLearning is a namespace that contains interfaces and classes for different algorithms of machine learning.

Best ROI in machine learning framework - G2 User in Internet

Ease of use

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Ease of Setup

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7.0

★★★★★

mlpack

mlpack is a scalable machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms, these algorithms as simple command-line programs and C++ classes which can then be integrated into larger-scale machine learning solutions.

ML Library for C++ lovers - Gunank S.

Ease of use

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7.0

★★★★★

Simple Bayes

Simple Bayes is a Naive Bayes machine learning implementation in Elixir.

Very helpful in designing Bayesian nw - G2 User in Financial Services

Ease of use

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7.0

★★★★★

Apache SAMOA

Apache SAMOA is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms it enables development of new ML algorithms without directly dealing with the complexity of underlying distributed stream processing engines (DSPEe, such as Apache Storm, Apache Flink, and Apache Samza) users can develop distributed streaming ML algorithms once and execute them on multiple DSPEs.

In my experience, this system is user-friendly and works well overall. - Carlin T.

Ease of use

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