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Machine Learning Software

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Up-to-date listing of Machine Learning Software to assist you throughout your software selection and purchase journey.

Machine Learning Software:

Machine Learning Software is a software that is used to extract insights from data and create logical models based on these insights.
The software can use these models to future process automation. Machine learning is a form of Artificial intelligence that enables a system to learn from data rather than through explicit programming and the software that uses this technology is known as Machine Learning Software.

Types of Machine Learning:

The various types of Machine Learning are Supervised, Unsupervised, Reinforcement and Deep learning.

  • In supervised machine learning, past data is used to make predictions. For example, the system makes predictions about an email as for whether it is a spam or not based on the previous data like received emails, data that we use etc. The machine learning algorithms that come under Supervised machine learning are Classification and Regression.
  • The hidden patterns can be found by using Unsupervised machine learning. It can be used when the problem requires a massive amount of unlabeled data such as social media applications. The machine learning algorithms that come under Unsupervised machine learning are Clustering and Association   
  • For improving or increasing efficiency, Reinforcement machine learning is used which is a behavioral learning model. Feedback can be received from the data analysis by the algorithm, guiding the user to the best outcome.
  • Deep learning incorporates neural networks in successive layers to learn from data in an iterative manner. Deep learning is useful when you are trying to learn patterns from unstructured data. Computers can be trained to deal with poorly defined abstractions and problems by deep learning as the complex neural networks can be designed to emulate how the human brain works.

Features and Benefits of Machine Learning Software:

The Machine Learning Software has several features that can benefit the user in many ways to grow their business. Some of these include:

  • The data connectivity needed to collect, analyze, manage and integrate asset data at scale can be provided.
  • Data from all sources including PLC, SCADA, MES, Historian and sensors can be collected and normalized. The ready to use data can then be send to any cloud or enterprise application to provide a complete data picture to improve industrial processes.
  • Flexible and scalable edge platform can be provided.
  • Data can be analyzed and managed at the Source. Real time data collection and processing can be done.
  • Data can be visualized with pre-built analytics and ready to use data can be send to any cloud or enterprise application.
  • A complete data picture can be achieved as data can be collected, analyzed, managed, and integrated with all-in-one platform.
  • The edge connectivity and intelligence needed to enable dozens of use cases ranging from predictive maintenance and asset condition monitoring can be provided to machine learning and industrial IoT.
  • Ready to use edge data can be collect, normalized, and send to any cloud, AI, big data, data lake or IT system for advanced analytics and machine learning.
  • The accuracy of predictive models can be improved. Accurate and better decisions can be made at a high speed
  • Models and algorithms can be provided for Classification, Regression, Clustering, Dimensional reduction, Model selection, and Pre-processing.
  • The software helps in data mining and data analysis.
  • A variety of optimization algorithms can be provided for building neural networks. Distributed training, various tools, and libraries can be provided.
  • The software helps in creating computational graphs.
  • A collaborative machine-learning platform can be provided for teams to explore, model and deploy data solutions, using the top open-source tools.
  • The hidden value can be extracted from enterprise data and can be quickly ingested and transformed data to create, deploy and manage high-accuracy self-learning models.
  • Users can be provided with access to actionable insights from all the data that helps achieve better business outcomes.

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