Machine Learning is a subset of Artificial Intelligence, used in developing applications that learn from experience through pattern identification and make decisions depending on the learning without being explicitly programmed for it. Machine Learning eliminates the hassle of programming a computer to make a decision. Instead, the machine learns from historical data and delivers better accuracy in decision-making over time through continuous learning.
Machine Learning is not a new concept, but it has come a long way since its inception. With the increasing volume of available data, computational processes that are efficient but consume limited resources for data mining and interpretation are in demand. Machine Learning is a technology that fits perfectly into this requisite.
Machine Learning methods
There are three distinct styles in Machine Learning. Each differs in the way the algorithms are trained.
Supervised Machine Learning: In this method, a labelled data set is used for training. Here both the input and output are specified, and the label information is used by the machine learning model to determine or classify the data.
Unsupervised Machine Learning: In the unsupervised method, a bulk amount of unlabelled data is fed into the model which is then crunched by the algorithms for extracting meaningful information that can be used for classifying or labelling data in real-time. Here the algorithm establishes connection between the data sets after scanning.
Semi-supervised Machine Learning: This efficient learning style bridges between the supervised and unsupervised methods. A smaller labelled data set is used while training which works as a guideline for classifying the unlabelled data set.
Reinforcement Machine Learning
In this model, sample data or historical data is not used for training the algorithm. Instead, the model keeps on learning in real-time through repeated trial and error. There are defined rules that guide the algorithm. It is typically used for training machines for handling multi-step processes.
It is a subset of Machine Learning that uses a hierarchical level of Artificial Neural Networks for learning how human brains work and process data. Typically, these models follow supervised or unsupervised learning methods. Reinforcement machine learning can also be used. The algorithms learn from unlabelled and unstructured data without human supervision.
Raybiztech also offers customized GIS mapping and data-point representations by engaging a well-equipped team of machine learning professionals. Our center of excellence extends to specialized analysis and interpretation of data with deep learning models that can also be leveraged for Artificial Intelligence (AI).
With a sound understanding of machine learning applications, Raybiztech provides consulting and development services in the upcoming areas of machine learning. We can leverage the behavior of computing assets to drive processes and enable our clients to use system resources in an optimal manner. We have built centers of excellence around Instruction-set languages and futuristic capabilities to serve our clients and stakeholders across a variety of domains and verticals.
Machine Learning Solutions
Machine Learning with Raybiztech
We at Raybiztech specialize in delivering end to end Machine Learning consultancy and development services. Our team of Ruby and Python developers is apt in executing critical projects that leverage the power of machine learning for maximum efficiency. We use the technology to enable optimum use of resources. From customized GIS mapping to data-point representations, fraud detection, medical image analysis, or chatbot development, our customized solutions explore the full gamut of deep learning models to deliver efficient, target-oriented solutions to meet diverse client needs.
With over 11 years in business and 400+ satisfied customers globally, Raybiztech is an industry leader in delivering advanced Machine Learning solutions.