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Feature Engineering in H2O Driverless AI - Dmitry Larko - H2O AI World London 2018

Sri Ambati
Sri Ambati
Sri AmbatiCEO & Founder at H2O.ai

This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/d6UMEmeXB6o In his talk Dmitry is going to cover common feature engineering techniques used to build robust machine learning models as well as some not widely known/used approaches. Bio: Senior Data Scientist at H2O.ai, Dmitry Larko also is a former #25 Kaggle Grandmaster and loves to use his machine learning and data science skills in Kaggle Competitions and predictive analytics software development. He has more than 15 years of experience in information technology. Post his masters in computer information systems from Krasnoyarsk State Technical University (KSTU), he started his career in data warehousing and business intelligence and gradually moved to big data and data science. He holds a lot of experience in predictive analytics in a wide array of domains and tasks. Prior to H2O.ai, Dmitry held the position of SAP BW Developer at Chevron, Data Scientist at EPAM, and that of Lead Software Engineer with the Russian Federation.

Feature Engineering in H2O Driverless AI - Dmitry Larko - H2O AI World London 2018

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Feature	engineering	
in	Driverless	AI
Dmitry	Larko
Senior	Data	Scientist
H2O.ai
6x	
"Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine
learning” is basically feature engineering." ~ Andrew Ng
Feature	Engineering
“… some machine learning projects succeed and some fail. What makes the difference? Easily the
most important factor is the features used.” ~ Pedro Domingos
“Good data preparation and feature engineering is integral to better prediction” ~ Marios Michailidis
(KazAnova), Kaggle GrandMaster, Kaggle #2, former #1
”you have to turn your inputs into things the algorithm can understand” ~ Shayne Miel, answer to
“What is the intuitive explanation of feature engineering in machine learning?”
6x	
"Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine
learning” is basically feature engineering." ~ Andrew Ng
Feature	Engineering
“… some machine learning projects succeed and some fail. What makes the difference? Easily the
most important factor is the features used.” ~ Pedro Domingos
“Good data preparation and feature engineering is integral to better prediction” ~ Marios Michailidis
(KazAnova), Kaggle GrandMaster, Kaggle #2, former #1
”you have to turn your inputs into things the algorithm can understand” ~ Shayne Miel, answer to
“What is the intuitive explanation of feature engineering in machine learning?”
What	is	feature	engineering
6x	
Not	possible	to	separate	using	linear	classifier
What	is	feature	engineering
6x	
What	if	we	use	polar	coordinates	
instead?
6x	
What	is	feature	engineering
6x
Ad

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