Profile
Name
IDEAS
Description
IDEAS is the abbreviation of International Data Engineering and Science Association (formerly Data Science Association). We build a data science hub to connect real-world knowledge and provide robust resources for business leaders, professionals, academics and promising students to thrive in this field.
This channel is created to spread ideas and presentations shared by our guest speakers.
Website: https://www.joinideas.org/
Facebook: www.facebook.com/ideassn/
Twitter: twitter.com/socal_ideas
Linkedin: www.linkedin.com/in/ideassn/
This channel is created to spread ideas and presentations shared by our guest speakers.
Website: https://www.joinideas.org/
Facebook: www.facebook.com/ideassn/
Twitter: twitter.com/socal_ideas
Linkedin: www.linkedin.com/in/ideassn/
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Channel Comments
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YungGudGalaxy
(3 minutes ago)
Here's a list for math concepts needed for Data Science, list is very exhaustive and is very dependent on what you're doing in the field but it gives a general idea of what areas you need to study.
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day2day82
(10 minutes ago)
I started the same way. I am a physicist and nanotechnologist, did PhD in electrical engg. Now working as a professor in machine learning and AI
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marklittlewood2418
(17 minutes ago)
He makes a very good point towards the end for those who are average or less with Math. Imagination and creativity with data is a big area and I can guarantee that you will find Math and non Math people who are good in this area. In some ways this is the most important area because coming up with models that are better than Joe's is usually because you thought of a different way of looking at the data or maybe you felt that some combination or feature engineering would produce better results.
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Dibbbbaaaaa
(28 minutes ago)
Priyanka Roy, Head of Data & Artificial Intelligence at Intergen talks about vision for your AI strategy on Engati CX. She says that companies should spend more time to know what the problem is, work on it and the user and then bring in the technology. She also mentions that data is extremely important and every AI strategy should be backed by data.
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jasonavina8135
(31 minutes ago)
this is actually a really excellent lecture on the topic, most lectures aren't even a quarter as concise, funny, and as informative as this speaker was. Its really an ideal introduction to the topic.
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ThunderGaming-ck4ey
(46 minutes ago)
Some of the best 23 mins I ever spent on YouTube.
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jaffreyjoy
(52 minutes ago)
Slides - http://zwmiller.com/blogs/ZWM_6MonthsToMachineLearning.pdf
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jorgeestebanmendozaortiz873
(2 hour ago)
Step 1. Get a PhD in Nuclear Physics.
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abhinav32100
(2 hour ago)
Summary is here:
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amirshehzad3352
(2 hours ago)
Slides:
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clinton11994
(2 hours ago)
this genius is a part of the mega hadron collider project !!! man ! they have the best minds on the planet ! damn !
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jeremyloscheider833
(12 hours ago)
Machine Learning Mastery us run by Jason Browne, who is in New Zealand, I think. I read his blog often.
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another.nikhil
(6 hours ago)
23 min well spent.
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TripleLayerLemonCake
(20 hours ago)
started a few weeks ago and learning linear algrebra rn. Ill check in every month or so to provide progress and maybe tips. Luckily I wont need to spend long on python as I know most of it, but might as well play with it for a few weeks.
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jantube358
(23 hours ago)
All in all, it was worth to watch the talk. 19:50 This question and answer was very interesting for me because I'm also not from a mathematical background but I have experience in designing business models and creative approaches to various problems. I got interested into ML myself because I don't know anybody else who knows something about it.
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