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