Best Machine Learning Software

Machine learning algorithms make predictions or decisions based on data. These learning algorithms can be embedded within applications to provide automated, artificial intelligence (AI) features or be used in an AI platform to build brand new applications. In both cases, a connection to a data source is necessary for the algorithm to learn and adapt over time. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others.

These learned algorithms may be developed with supervised learning or unsupervised learning. Supervised learning consists of training an algorithm to determine a pattern of inference by feeding it consistent data to produce a repeated, general output. Human training is necessary for this type of learning. Unsupervised learning, on the other hand, requires no consistency in the input of machine learning algorithms. Unsupervised algorithms independently reach an output and are a feature of deep learning algorithms. Reinforcement learning is the final form of machine learning, which consists of algorithms that understand how to react based on their situation or environment. For example, autonomous driving cars are an instance of reinforcement machine learning because they react based on their surroundings on the road. If a traffic light is red, the car stops. Machine learning algorithms are used by developers when using an AI platform to build an application or to embed AI within an existing application. End users of intelligent applications may not be aware that an everyday software tool is utilizing a machine learning algorithm to provide some form of automation. Additionally, machine learning solutions for businesses may come in a machine learning as a service model.

To qualify for inclusion in the Machine Learning category, a product must:

  • Offer an algorithm or product that learns and adapts based on data
  • Be the source of intelligent learning capabilities for applications
  • Consume data inputs from a variety of data pools
  • Provide an output that solves a specific issue based on the learned data
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    Microsoft Knowledge Exploration Service is a service that offers a fast and effective way to add interactive search and refinement to applications, it allows user to build a compressed index from structured data, author a grammar that interprets natural language queries, and provide interactive query formulation with auto-completion suggestions.

    Use your own data to create, train, and deploy machine learning and deep learning models. Leverage an automated, collaborative workflow to grow intelligent business applications easily and with more confidence.

    Recommendations API is a tool that helps customer discover items in users catalog, customer activity in a user's digital store is used to recommend items and to improve conversion in digital store.

    Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

    Cloud TPU empowers businesses everywhere to access this accelerator technology to speed up their machine learning workloads on Google Cloud

    Our platform leverages human-in-the-loop practices to train, test, and tune machine learning models. At Figure Eight, we know that AI isn’t magic. We know what it takes to create AI that isn’t just a science project, but AI that works in the real world. And we provide the crucial ingredients that make it happen. We believe that AI is the combination of three important components: training data, machine learning, and humans-in-the-loop.

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

    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.

    Microsoft Bing Autosuggest API is a tool that help users complete queries faster by adding intelligent type-ahead capabilities to an app or website.

    machine learning support vector machine (SVMs), and support vector regression (SVRs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

    Enjoy the power of Programmatic Machine Learning

    Microsoft Cognitive Toolkit is an open-source, commercial-grade toolkit that empowers user to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms already use.

    Qubole
    (32)3.9 out of 5
    Optimized for quick response
    Optimized for quick response

    Qubole is revolutionizing the way companies activate their data--the process of putting data into active use across their organizations. With Qubole's cloud-native Data Platform for analytics and machine learning, companies exponentially activate petabytes of data faster, for everyone and any use case, while continuously lowering costs. Qubole overcomes the challenges of expanding users, use cases, and variety and volume of data while constrained by limited budgets and a global shortage of big data skills. Qubole's intelligent automation and self-service supercharge productivity, while workload-aware auto-scaling and real-time spot buying drive down compute costs dramatically. Qubole offers the only platform that delivers freedom of choice, eliminating legacy lock in--use any engine, any tool, and any cloud to match your company's needs.

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

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

    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 innovation that brings the best of open-source and proprietary worlds together

    Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google's state-of-the-art transfer learning, and Neural Architecture Search technology

    Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks.

    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.

    Microsoft Academic Knowledge API is a service that allow user to interpret queries for academic intent and retrieve rich information from the Microsoft Academic Graph (MAG), it is a knowledge base web-scale heterogeneous entity graph comprised of entities that model scholarly activities: field of study, author, institution, paper, venue, and event.

    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.

    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

    The ML-Agents SDK allows researchers and developers to transform games and simulations created using the Unity Editor into environments where intelligent agents can be trained using Deep Reinforcement Learning, Evolutionary Strategies, or other machine learning methods through a simple to use Python API.

    FloydHub is a platform specially designed for deep learning and eliminating the engineering bottlenecks.

    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 ** .

    SAS Enterprise Miner is a software provide insights that drive better decision making, it streamline the data mining process to develop models quickly, understand key relationships and find the patterns that matter most.

    Pylearn2 is a library for machine learning research.

    Protégé is supported by a strong community of academic, government, and corporate users, who use Protégé to build knowledge-based solutions in areas as diverse as biomedicine, e-commerce, and organizational modeling.

    Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing.

    pyBrain is a modular machine learning library fr python that offer a flexible, easy-to-se and powerful algorithms for machine learning task and a variety of predefined environments to test and compare algorithms.

    mlr: Machine Learning in R that interface to a large number of classification and regression techniques, including machine-readable parameter descriptions.

    Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point and it creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.

    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, statistics, data handling, etc.

    PrediCX is a predictive analytics engine designed to take all heterogeneous data and process it dynamically to make recommendations to operators in terms of 'next best action' based ultimately on optimising the customer experience (CX).

    clj-ml is a machine learning library for Clojure that can be applied to data sets to modify the dataset in some way: transforming nominal attributes into binary attributes, removing attributes etc.

    DataRobot is the premier platform for automated machine learning. With a library of hundreds of the most powerful open source machine learning algorithms, DataRobot automates feature engineering, model creation, and hyperparameter tuning to expedite the deployment of advanced AI applications. The platform encapsulates every best practice and safeguard to help organizations accelerate and scale their data science capabilities while maximizing transparency, accuracy and collaboration between trained data scientists and empowered citizen data scientists.

    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.

    IBM Watson Personality Insights is a tool that extracts and analyzes a spectrum of personality attributes to help discover actionable insights about people and entities, and in turn guides end users to highly personalized interactions.

    Algorithmia
    (3)4.5 out of 5
    Optimized for quick response
    Optimized for quick response

    Algorithmia is DevOps for machine learning. We power the largest algorithm marketplace as well as mission-critical workloads for enterprise customers. Our technology is trusted by close to 100,000 developers as well as many financial institutions, intelligence agencies, and private companies leveraging AI/ML at scale. Productionizing ML requires a different set of IT infrastructure and workflows than traditional programming. Algorithmia solves this challenge with the AI Layer, an abstraction layer that connects your models, hardware, and applications. The AI Layer allows you to deploy models from any framework, language, or platform and connect to most all data sources. We scale model inference on cloud or on-premises infrastructure with high efficiency and enable users to continuously manage the machine learning life cycle with tools to iterate, audit, secure, and govern. Algorithmia was founded in 2014 by Diego Oppenheimer and Kenny Daniel and is headquartered in Seattle, Washington. It completed a Series B round of funding in May 2019, raising $25 million. Algorithmia currently employs 40-45 people and is growing rapidly.

    BioPy is a collection of biologically-inspired algorithms written in Python that are more focused on artificial model's of biological computation, such as Hopfield Neural Networks, while others are inherently more biologically-focused, such as the basic genetic programming module included in this project.

    CloudForest allows for a number of related algorithms for classification, regression, feature selection and structure analysis on heterogeneous numerical / categorical data with missing values.

    Dataiku is the centralized data platform that moves businesses along their data journey from analytics at scale to enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies. Customers across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuticals, and more use Dataiku to power self-service analytics while also ensuring the operationalization of machine learning models in production. By removing roadblocks, Dataiku ensures more opportunity for business-impacting models and creative solutions, allowing teams to work faster and smarter.

    Google Cloud ML Engine is a managed service for creating ML solutions that provides ML model building and training, predictive analytics, and deep learning.

    htm.java is a Hierarchical Temporal Memory implementation in Java - an official Community-Driven Java port of the Numenta Platform for Intelligent Computing (NuPIC) it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation; while observing the tenets, standards and conventions of Java language best practices and development.

    Learning Based Java is a modeling language for the rapid development of software systems with one or more learned functions, designed for use with the JavaTM programming language that offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application.

    MAChineLearning is a framework that provides a quick and easy way to experiment with machine learning with native code on the Mac. It is written in Objective-C and it is usable by Swift.

    Naive Bayesian Classification for Golang that perform classification into an arbitrary number of classes on sets of strings.

    Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data that leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

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