{"id":2216,"date":"2018-03-14T22:26:22","date_gmt":"2018-03-14T21:26:22","guid":{"rendered":"http:\/\/mocnedata.sk\/en\/blizi-sa-data-science-club\/"},"modified":"2018-06-08T23:24:41","modified_gmt":"2018-06-08T21:24:41","slug":"feature-engineering-berlin","status":"publish","type":"post","link":"https:\/\/mocnedata.sk\/en\/feature-engineering-berlin\/","title":{"rendered":"Unconventional methods of Feature engineering"},"content":{"rendered":"

My dear fellows,<\/p>\n

after settling down in Berlin Datascience community and visiting few interesting, local meet-ups, I agreed to contribute to the community knowledge sharing as well<\/strong>. Thus, if you have a while you can join Sebastian Meier<\/a>\u00a0[Technologiestiftung Berlin], Sandris Murins<\/a>\u00a0[Iconiq Lab] and me<\/a> on June 14th 2018 18:00 at AI IN DATA SCIENCE – BERLIN<\/a>\u00a0at Wayfair DE Office, K\u00f6penicker Str. 180, 10997 Berlin, Germany.<\/span><\/span><\/p>\n

To ensure you that it really pays off to reserve some time for this meet-up let me make a sneak-preview<\/strong><\/span>\u00a0of what you might\u00a0get from this event:<\/p>\n

\"BERLIN<\/p>\n

\"COMING_SOON\"In my\u00a0presentation, titled\u00a0\u00a0“Feature <\/b>engineering<\/b><\/span>\u00a0– your trump card in Machine Learning<\/b><\/span>”\u00a0\u00a0you\u00a0will find out\u00a0what\u00a0are the 2 key reasons<\/strong>\u00a0for feature engineering future. First\u00a0explains how\u00a0variables can become competitive advantage of your models. The second one goes even beyond that and hopefully opens your eyes why Feature engineering might be actually essential for\u00a0Data Scientists’\u00a0future<\/strong> job security.<\/p>\n

After getting warmed up on the overall role of features,\u00a0we shall\u00a0jump into how variables are actually designed\/generated in real life. What are the most\u00a0used\u00a0 approaches <\/strong><\/span>to design the features? But more importantly, what are the common pitfalls<\/strong> <\/span>that we all often fall into while designing our feature sets for Machine Learning predictions? Are there\u00a0tricks\u00a0to prevent<\/strong> us from failing on these ?<\/p>\n

\"uncoventional\"<\/p>\n

However, just to\u00a0demote the traditional ways and\u00a0solely point out their notorious downsides without naming the alternative(s) would be a bit unfair. Therefore, in second half of my talk I want to walk you through unconventional ways how to design your features.<\/span><\/strong> What is more, I will take help of 4 specific case studies<\/strong> where unconventional features were needed. I hope you might get inspired for your own work.<\/p>\n

If you cannot make it (for whatever reason) to this talk and you are member of MighytDataCommunity<\/a>, you will find the presentation slides here after event, in member-only-restricted blog post. If you are not member of the free MightyData Community yet, you can get your free membership\u00a0to this community in less then 2 minutes, by registering here<\/strong><\/span><\/a>.<\/p>\n

Looking forward to seeing you all at the AI Meet-up event<\/a>\u00a0!<\/span><\/strong><\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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