Machine Learning

Machine learning (ML) is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.
The name machine learning was coined in 1959 by Arthur Samuel.Machine learning explores the study and construction of algorithms that can learn from and make predictions on data - such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,: through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[ where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.

Sample Architecture

Google ML Kit

Google ML Kit is Google�s machine learning beta SDK for mobile developers. It allows developers to use machine learning to build features on Android and iOS, whatever the level of expertise. The ML Kit includes five base APIs that are ready to use across common mobile use cases. These include text recognition, face detection, barcode scanning, image labelling and landmark recognition � all of which are available both online and offline. Developers are also able to deploy their own TensorFlow Lite models in case the provided APIs do not suit their use case. These can be uploaded directly via the Firebase console, where the SDK platform is based.

HPE Haven OnDemand

HPE Haven OnDemand provides high-level machine learning APIs for enterprise app developers. It has over 70 different APIs available, they range from face detection, image classification, speech and object recognition, text analysis and more. It is hosted on Microsoft Azure and has a number of API client libraries for developers to apply machine learning to apps easily.

Amazon Machine Learning

Amazon Machine Learning offers a managed service for developers and data scientists building machine learning models and generating predictions. It enables the development of robust, scalable smart applications that can be used without the need for an extensive background in machine learning algorithms and techniques. The service consists of three operations that are provided for the machine learning models building process. These are data analysis, model training, and evaluation. Its features also include APIs for batch and real-time predictions to enable users to easily build smart

Azure Machine Learning Workbench

Microsoft's announced a revamp of its Microsoft Azure machine learning tools during its 'Ignite' conference in September 2017. Microsoft announced three major machine learning tools, one of which is the Azure Machine Learning workbench, described as a cross-platform client for data and experiment management. The workbench will support modelling in Python, Scala and PySpark.

Apache PredictionIO

Apache PredictionIO is an open source machine learning server, built on top of an open source stack for developers and data scientists to create predictive engines for all machine learning tasks. It can be installed as a full machine learning stack, together with Apache Spark, MLlib, HBase, Spray, and Elasticsearch in order to simplify and accelerate machine learning infrastructure management. A unique feature of PredictionIO is its ability to respond to dynamic queries in real-time once deployed as a web service, whilst also unifying data from multiple platforms in batch or real-time to gather comprehensive predictive analytics. PredictionIO also provides a template system for creating machine learning engines. These reduce the traditional form of heavy lifting to set up the system and serve specific kinds of predictions.

Accord.NET

Accord.NET is a framework for scientific computing in .NET. It is combined with audio and image processing libraries which encompass a range of scientific computing applications such as machine learning, statistical data processing and pattern recognition. Additionally, it can be described as a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications. Following the merger with the AForge.NET project in 2015, the framework has since offered a unified API for learning and training machine learning models. Accord.NET can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux and Mobile.