Disclaimer: None of what follows is an actual trading advice. It was meant to educate and share my learning experience with the community, and should not be used as-is for making assumptions about the future direction of any market asset. Always do your own research!
This article is a follow-up transcription to a talk I recently gave at a local Munich machine learning meetup. Unlike my previous talk, this time I wanted to convey the idea of using an iPad for actively running data science experiments, as opposed to passively consuming information. I illustrated my point with a few example iOS applications I personally use on a daily basis, which I hope would be good starting points to get the audience interested in the idea. Being an iOS developer, who has once built ...
My crypto-obsession from last year might have died down a little, but I still remain a long-term investor and a believer of decentralization. Moreover, it was predicting the crypto market’s next move that made dust off some of my old machine learning experiments and gave a fresh boost to my mission of becoming a data scientist. Though one could hardly hear me speak about this coin or that, I am still actively researching and building tools.
Often, we will want to get to get a specific row, which marks the minimum or maximum of one of its columns. Let’s suppose we have the SF Salaries dataset from Kaggle. We want to find the employee name, with the largest total pay benefits. The experience with writing NumPy/Pandas filter conditions will quickly let us produce the following version: