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I'll add my thoughts on the topic of "how do you know when you're out of the danger zone and can start marketing yourself as a machine learning expert?" Do com
by olympus 11y ago
I'll add my thoughts on the topic of "how do you know when you're out of the danger zone and can start marketing yourself as a machine learning expert?"
Do competitions on kaggle (or find them on other sites, but kaggle is definitely the best place to start). Once you get past the point where you are finishing in the middle of the pack (multiple top 10% or 25% finishes and maybe a prize win) then you are an expert. That is proof that you are separate from than hackers who just throw scikit-learn algorithms at a matrix. The people in the master tier use clever feature engineering and/or code up custom learning algorithms to get themselves above the masses. Looking at a problem and figuring out the correct modeling approach is what the experts do. They don't just create a data frame and run down the list of classification algorithms that they have access to. Read the "No Free Hunch" reports on how the winners did it and you'll quickly see the difference between yourself and the experts.
- hangtwenty 11y agoThis is a fantastic contribution, thank you! I opened an issue ... https://github.com/hangtwenty/dive-into-machine-learning/issues/11 https://github.com/hangtwenty/dive-into-machine-learning/iss... I'll wait a bit in case you want to add a note on this in your own words (via PR). Otherwise, tonight or tomorrow I'll paraphrase you or something. Whether you or I make the change I want it in a branch, and then I'll try to get a bit of review for that branch ... THANKS AGAIN, the guide really needs some insight like this.
- rodneyfool 11y agoCan you give an example of "clever feature engineering"? I'm pretty much in the hacker space right now, able to choose a pre-built algorithm based on the problem, but I hear this talk about clever feature engineering quite a lot. Is this mostly just picking the right data to work with? Thanks!