"IVA, an organisation led by eminentprofessionals that have a vast experience and expertise, intends to inculcate a data-driven culture, helping organizations interpret an ocean of quantifiable data to produce actionable business insights. Located in Kerala's capital city, with affiliates across the globe, reputed clientele and infinite successful projects, IVA deploys innovative Predictive and Business analytics, to aid businesses make informed decisions and ensure improved business performance.
From retrieving data to analysing and processing it, our advanced Predictive analytics tools can be utilized for the discovery, evaluation, and deployment of predictive scenarios. The unique scalable technology can be applied to any business model across industries, from small to large organizations.
Identifying the stumbling blocks in business, IVA with its cognitive technologies, that combine Artificial Intelligence and Machine Learning, enables organizations to substantially transform themselves.
IVA’s Artificial Intelligence capabilities promise:
Automating business processes
Gaining insights through data analysis
Engage with customers and employees
The cognitive insights provided by Machine Learning are a notch higher and are exceedingly data-intensive and detailed, and the predictions based on the new data are more accurate and better.
IVA’s unique transformational solutions are not just about improving business operations but also about optimizing them along with substantial cost reductions.
IVA intends to familiarize organizations with cognitive tools, and experiment with projects that combine elements of AI & ML. This will help to reap significant benefits that will empower them to be resilient and be future-ready."
International Virtual Assistance
Pvt Ltd. B-10, Park Center Building,
Phase I, Technopark Campus,
TVM 695 582.
Ph: +91 6238 642 100,
8089 070 851.
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Advantages of Machine Learning (4).mp4
Building machine learning models that can generalise well on future data necessitates careful analysis of the data at hand as well as assumptions regarding the different available training algorithms.
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