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14 Sep 2017   |    Views : 1120    ; ; ;   |    ;

As part of The Math Behind ML series, we had two sessions at BHIVE Residency Road. The last and final session will happen at the same venue this Friday. The topic is "When and How to Use Probability Distributions".

Bangalore is buzzing with events in Data Science, Machine Learning, and Artificial Intelligence. However, most of these meetups and workshops use only black-box models. This is something like learning to drive without knowing the mechanics. In the context of Machine Learning, this is not enough when you are trying to solve non-standard problems. To solve non-standard problems you require a deeper understanding of the math behind Machine Learning. This three-part hands-on workshop series consists of short sessions to introduce and explain the math that drives Machine Learning.

This workshop serious is brought to you by Devopedia. It's led by See Thru Data Analytics and Insights, a start-up that provides ML as a service to businesses. The workshop is best suited for beginners in Machine Learning or those not from statistical backgrounds. The workshop will help those who wish to switch to Machine Learning with confidence.

We will use Excel during the hands-on sessions. Each session will be focused and we will dive deep into a particular topic. You will learn by working on sample data. In the process, ML techniques that have been "mysterious" will become more intuitive to you. The topics are independent of one another; so even if you miss one session you will be able to follow the next session.

Trainer

Ramanathan is a Data Evangelist with over a decade of experience contributing to Analytics functions in Retail/FMCG/Finance. He has extensively worked on various statistical modeling and machine learning models. He's a vivid follower of business strategies and geopolitics. He cherishes sharing knowledge, building organizations from scratch and mentoring ambitious individuals in various platforms. He is the Founder of See Thru Data Analytics and Insights.

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