added a guide #38
added a guide #38
Conversation
Fix typo.
an attribution that doesn't really seem useful because the person I attributed it to doesn't have much net presence. they only sent me a link anyway!
Update README.md
…n updated from BIOS366 to BIOS8366
There's a Talking Machines episode where Adams gets at some of the same points the guide emphasizes, from the Domingos paper. Add a tip about this.
Updated link to Advanced Statistical Computing Course
Fix Gideon Wulfsohn's website link
Remove branded attribution. It was not consistent with the other attributions/citations in this document
Just in case it boosts trust/ makes more people click through and try the course
We already had links to his General Assembly Data Science course materials ... Add related links to his video series on machine-learning and scikit-learn. Addresses #34 opened by Markham himself :)
Add links to other resources by Kevin Markham
I forgot to add "Prof." somewhere. Should be consistent :)
Thanks to @numberwhun for sending me the link :)
Relates to #36.
sensitive to order of arguments I guess
I started going through and fixing up the redirects but enough of them seem RIGHT that I lost interest. Sometimes the URL that redirects is actually preferable and I just don't want to do a whole whitelisting thing Closes #37 (as WONTFIX)
Followed the "tldr" section at https://docs.travis-ci.com/user/migrating-from-legacy/ . Nice.
This guide sets pragmatic philosophy towards becoming a machine learning developer.
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@vishwajeetv there is some really great advice in this guide. I love the pragmatic focus. I will show this article to at least one coworker tomorrow 😄 "Dive into Machine Learning" is very carefully curated so far ... While the Brownlee article has a similar hack-first focus, I have issues with Brownlee's tone. I'm trying to put myself in the shoes of someone who has more experience with ML, and did get a PhD in it. Maybe this is my issue: this article provides advice that may help some developers get into machine learning ... yet it has a lot of attitude, and that attitude is discouraging towards inquiry and curiosity. If everyone had the attitude of this article (at least, at some of its moments) ... there would be no machine learning field for developers to get into; nobody would get into math or research let alone something as "un-pragmatic" as machine learning research! I will mull this over a bit. Sorry to delay :) Appreciate the PR, for sure |
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Opening a dialogue with the author. A bit bold maybe but it's worth a shot. I'll follow up on other channels if Twitter doesn't work. |
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(build failure is erroneous, see https://github.com/dkhamsing/awesome_bot/issues/46 ) |
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Sent an email to the author, let's see |
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Well, unfortunately the author was not receptive. It's a shame. The article has really good advice, but I have to wrap it in so much defensive language to feel like it's appropriate to include -- with certain readers in mind. I really don't want to alienate those readers. Hmm. |
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@vishwajeetv I'm sorry, I rebased my history and I didn't realize it was going to cause a mess; would you mind re-submitting this pull request in a new fork off latest master? I know it's just a 1-line thing but I want you to be on the contributors list like you should be :) |
This guide sets pragmatic philosophy towards becoming a machine learning developer.