Internalizing Machine Learning
Internalizing Machine Learning


The interest in Machine learning is increasing with every passing day. Enterprises all over the world are exploring possibilities of having machine learning solutions to overcome business challenges and to unleash the many hidden insights of the data.

The global machine learning market is estimated to be at USD 20 billion by 2024.

As the name suggest Machine learning involves systems learning from data using algorithms that iteratively learn and provide insights without being programmed where to look for.
Having all the benefits on one side, the major challenge organizations face is to internalize Machine learning within the existing setup.
The solution to this lies to have machine learning solutions approachable and easy to be scaled within an organization so that it helps in a large collaborative effort, going away from the traditional approach of working in silos.

So, the session today will be to demonstrate some of the popular algorithms used in machine learning by organizations, using SAS. The demo would take you through combining structured and unstructured data, build and compare machine learning models build in SAS and developed using open source languages. To end the process also create score codes for implementing predictive models.

Who should Attend: Data Analyst, Data Scientists, Students Pursuing Masters in the Field of Statistics, Engineering, Mathematics, Economics, Business Analysts


Akshay is Analytics Consultant, Education at SAS India. In his current role, Akshay works with various clients of SAS in the Asia Pacific region to develop workforce in effective use of the SAS products in the area of Analytics, data management and Business data visualization. He has worked with customers in Financial Services, Insurance, Manufacturing and Telecommunication.

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