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RESOURCES | ASPIRING MINDS RESEARCH Resources. AMEO. Data sets, tools and APIs. Aspiring Minds’ Employability Outcomes 2015 (AMEO 2015) is a unique dataset which contains engineering graduates’ employment outcomes (salaries, job titles and job locations) along with standardized assessment scores in three fundamental areas – cognitive skills, technical skills andpersonality.
ASPIRING MINDS RESEARCH Aspiring Minds aspires to build an assessment-driven job marketplace (a SAT/GRE for jobs) to drive accountability in higher education and meritocracy in labor markets. The products developed based on our research have impacted more than two million lives and the resulting data is a source of continuous new research. RESOURCES | ASPIRING MINDS RESEARCH Resources. Data sets, tools and APIs. AMEO. We are keen to see a vibrant research community around assessments and help them deliver social good. We put in our little bit to help the community by making some of our data sets and code/APIs available for academic use. We also actively collaborate with various groups and look forward toengage
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ASSESSMENT | ASPIRING MINDS RESEARCH Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to grade QUESTION INDEPENDENT GRADING USING MACHINE LEARNING: THE (a) Question dependent model training (b) Question independent model training Figure 1: Question independent model learning Fig (a) shows how question speci c models were learnt in .ASPIRING MINDS
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Data-driven decision making is fast becoming a necessary skill in jobs across the board. The industry today uses analytics and machine learning to get useful insights from a wealth of digital information in order to make decisions. RESOURCES | ASPIRING MINDS RESEARCH Aspiring Minds’ Employability Outcomes 2015 (AMEO 2015) is a unique dataset which contains engineering graduates’ employment outcomes (salaries, job titles and job locations) along with standardized assessment scores in three fundamental areas - ASPIRING MINDS RESEARCH AM Research is a division of Aspiring Minds.Aspiring Minds aspires to build an assessment-driven job marketplace (a SAT/GRE for jobs) to drive accountability in higher education andAM INTERVIEWS
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We're Sorry but Your Browser is Not Supported. It is highly recommended that you choose and install a modern browser. It is free of charge and only takes a couple of ASSESSMENT | ASPIRING MINDS RESEARCH Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to grade PROGRAMMING ASSESSMENTS Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to grade ASPIRING MINDSASPIRING MINDS AMAZONASPIRING MINDS SVAR TESTLoading. Loading
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We're Sorry but Your Browser is Not Supported. It is highly recommended that you choose and install a modern browser. It is free of charge and only takes a couple of ASSESSMENT | ASPIRING MINDS RESEARCH Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to grade PROGRAMMING ASSESSMENTS Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to gradeASPIRING MINDS
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Data-driven decision making is fast becoming a necessary skill in jobs across the board. The industry today uses analytics and machine learning to get useful insights from a wealth of digital information in order to make decisions.ONLINE AWS TEST
Online AWS aptitude and assessment test for employment and identifying high-performers. Short MCQ-based test to evaluate the knowledge of AWS delivery process, monitoring, metrics, logging, security, validation and scalability. Request your 14-day free trial today. RESOURCES | ASPIRING MINDS RESEARCH Aspiring Minds’ Employability Outcomes 2015 (AMEO 2015) is a unique dataset which contains engineering graduates’ employment outcomes (salaries, job titles and job locations) along with standardized assessment scores in three fundamental areas -ASPIRING MINDS
Important Instructions. This leaderboard shows your relative standing among other participants. This is not your final standing. Submissions made here are not final. PROGRAMMING ASSESSMENTS Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to grade ASSESSMENT | ASPIRING MINDS RESEARCH Machine learning has helped solved many grading challenges – spoken english, essay grading, program grading and math problem grading to cite a few examples. However, there is a big impedance in using these methods in real world settings. This is because one needs to build an ML model for every question/prompt – for instance, in essay grading, a different model designed to gradeML COMPETITION
Data challenge, 2015 (Released as a challenge at CoDS 2015) In this data challenge, we released AMEO 2015, our data set which profiles undergraduates with varied backgrounds and pose these challenges- Predictive Modeling Given a new student profile, can we predict his/her annual salary from historic data? Recommendation Can we identify the key set of parameters in his profile changing which, sASPIRING MINDS
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