Machine Learning and Data Sciences Conference | Fusion - WeRIndia

Machine Learning and Data Sciences Conference will be held in Bengaluru in August next week by software giant Microsoft. This is its maiden edition in India this year. The 2-day Conference emphasizes the significance of the Indian market with respect to future use of latest promising technologies. This event is expected to be helpful to the startups.

The event is going to be held on August 8 and 9 in Bengaluru. More than 600 participants will join in this Conference. These include government officials, developers, students, start-ups and manufacturing executives.

Microsoft India R&D Managing Director Anil Bhansali said that it is the time for the company to transform since they focus on mobile and cloud solutions and grand plans to influence the computing of future for their extensive users from all over the world who are in billions.

Machine Learning is one of the key focus areas as Microsoft has significant business in both research and developments and true customer impact. The R&D Managing Director added that they will present some latest developments in machine learning in the Conference so that one can learn about power analytics and machine learning in transforming businesses as well as society.

Machine learning is a type of artificial intelligence which facilitates to learn without being overtly programmed and also identifies the data and pattern so that the program actions can be adjusted consequently. Facebook’s News Feed which uses the technology to customize every member’s feed is one of the examples for machine learning.

Another example for machine learning is Microsoft’s voice assistant Cortana. Another similar event will be held by Microsoft in Atlanta in September. The software giant developed many apps using the latest technologies. Many of them are very useful to people. One of their apps is very helpful to farmers in India. It predicts the proper sowing dates for certain crops. Another app recognizes the individuals who are most likely to default on loans from financial institutions.

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