Analyttica Datalab Inc.

Rajiv Baphna

Ashutosh Joshi

Halasya Siva Subramania

Satyamoy Chatterjee

Apparatuses, systems, methods, and computer program products are disclosed for adaptive machine learning. An apparatus includes a monitoring module that continuously monitors one or more interactions of a user while the user performs one or more simulated tasks digitally presented to the user that are associated with a learning path. The apparatus includes a metadata module that tracks data describing the user's interactions during the user's performance of one or more simulated tasks. The apparatus includes a machine learning module that, dynamically and in real-time, optimizes the user's learning path by simulating multiple different learning paths using one or more machine learning processes and tracked data. The apparatus includes a recommendation module that presents one or more recommendations to the user for optimizing the user's learning path. One or more recommendations may be generated as a function of the optimized learning path.

Adaptive machine learning system

A processor implemented method for scoring a challenge taken by a user, and training the user using a simulation based learning platform is provided. The processor implemented method includes (i) obtaining, from a database, challenges to be taken by a user and associated at least one of information, (iii) processing a selection of, the challenge from the challenges with actions or steps performed by the user, (iv) rendering, hints to solve the challenge, (v) recording, steps taken by the user to solve the challenge, (vi) comparing the steps taken by the user with steps taken by an expert to solve the challenge to compute a deviance of the user from a reference path, (vii) scoring, the challenge based on the deviance of the user from the reference path to obtain a score, and (viii) notifying, a result associated with the challenge to the user based on the score.

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Bengaluru, India

Rajiv Baphna