Machine Learning within Reach for the Enterprise
Machine Learning within Reach for the Enterprise


If you are able to apply machine learning to solve your problems, you have a great competitive advantage but when it comes to machine learning, where do you really start and what's the right environment that you can operate?

Preparing your organisation for such initiatives is a serious undertaking and one should aim to identify right business objectives, remove silos and foster collaboration since it's about delivering valuable information to the right people when it's needed.

This talk will give you the perspective of a data scientist and answer important questions such as "What are the areas that I can apply machine learning?"", ""How to have well-defined problem that machine learning can solve?", "How I can codify my business rules into machine learning algorithms?"", ""What's the right environment that can support me throughout the whole process to succeed?"


Umit is a Data Scientist at IBM, extensively focusing on IBM Data Science Experience and IBM Watson Machine Learning to solve complex business problems. His research spans across many areas from statistical modeling of financial asset prices to using evolutionary algorithms to improve the performance of machine learning models. Before joining IBM, he worked on various domains such as high-frequency trading, supply chain management and consulting. He likes to learn from others and also share his insights at conferences, universities and local meet-ups

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