If your data is junk, no model, no matter how 'cool' the algorithm is, will give anything useful.
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But understanding the data, patterns in variables, how to fix them to get fully meaning variables is what matters at all. Models can easily be automated and run best on structured, cleaned data, and there are tools out there that do it with minimal effort. Data is the most important thing of machine learning. If you only care about one concept in a course, and just want to learn it without getting a certificate, that's totally fine!įocus on learning the skill, not on getting the certificate.Įxactly. But if you doesn't have the skill, it's useless.
I understand that having a certificate gives you a sense of accomplishment. Your projects matter, your knowledge matter, and your communication skills matter. Now when I start interviewing more candidates, I realize I never looked at the certificates.
But then I realized I still need to go back to read the course material, and it actually cost me more time in learning. I immediately put the certificate on Linkedin, I felt proud. I evaluated the certificate more than the knowledge.
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I googled the code I didn't understand and find the answer fast without understanding it thoroughly.
When I was eager to learn machine learning, I wanted to finish it as soon as possible. How I got a four-week Coursera machine learning course Certificate in two weeks (and why it was stupid). Data | Host of“The Data Scientist Show" Podcast
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