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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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Machine Learning reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.

Compare Machine Learning Software
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    Microsoft Bing Image Search API is a service that provides a similar (but not exact) experience to Bing.com/Images (overview on MSDN), it allow partners send a search query to Bing and get back a list of relevant images.


    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.


    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


    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.


    Microsoft Bing Web Search API is a service that retrieve web documents indexed by Bing and narrow down the results by result type, freshness and more, it bring intelligent search to apps and harness the ability to comb billions of webpages, images, videos, and news with a single API call.


    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.


    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


    Dialogflow is an end-to-end development suite for building conversational interfaces for websites, mobile applications, popular messaging platforms, and IoT devices.


    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 ** .


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


    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.


    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.


    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.


    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).


    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 DataRobot automated machine learning platform captures the knowledge, experience and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels – from business people to analysts to data scientists – to build and deploy highly-accurate machine learning models in a fraction of the time of traditional modeling methods.


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


    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.


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


    Microsoft Bing News Search API is a tool that search the web for news articles including details like authoritative image of the news article, related news and categories, provider info, article URL, and date added.


    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.


    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.


    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.


    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.


    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.


    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


    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.


    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.


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



    Pylearn2 is a library for machine learning research.


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


    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.


    AForge.Video is library that contains interfaces and classes to access different video sources, such as IP video cameras (MJPEG streams).


    Bolt is a discriminative learning of linear predictors (e.g. SVM or Logistic Regression) that uses fast online learning algorithms to aimed large-scale, high-dimensional and sparse machine-learning problems. In particular, problems encountered in information retrieval and natural language processing.


    GraphLab Create is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance data products.


    HLearn is a high performance machine learning library written in Haskell to discover the "best possible" interface for machine learning. This involves two competing demands: The library should be as fast as low-level libraries written in C/C++/Fortran/Assembly; but it should be as flexible as libraries written in high level languages like Python/R/Matlab.


    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.


    Intel Data Analytics Acceleration Library (or Intel DAAL) is a software development library that is highly optimized for Intel architecture processors it provides building blocks for all data analytics stages, from data preparation to data mining and machine learning.


    kernlab is a Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction and the method support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.


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


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


    Pattern is a web mining module for the Python programming language that has a tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization.


    Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations that can learn with observational and interventional data, handles missing values and hidden variables using exact and heuristic methods, provides several learning algorithms; makes creating new ones simple, has facilities for transparent parallel execution using several cluster and cloud resources, calculates edge marginals and consensus networks and presents results in a variety of formats.


    pyhsmm Bayesian inference in HSMMs and HMMs is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.


    Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles, suited for high-dimensional data.


    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.