I'm well aware. It comes down to the right combination of an algorithm and heuristics. And it's the heuristics part that is rarely transferable from problem to problem, the part you laid out in bullet points for your hired gun.
I'm just interested in understanding the methodologies, and "cherry-picking" what's applicable to my problems.
I purchased this early release, and like it so far.
Sadly, I keep finding myself going back to R for small data analysis instead of Pandas. Pandas may make the data transformation on par with, and computationally quicker than, R. But the lack of libraries in Python makes it difficult to work with for long periods of time.
I like the idea, a lot. This seems to be well executed, but I wonder how much of a future this thing has with XBRL becoming a requirement. In 5 to 7 years there will be enough historical data tagged in XBRL for any analyst to perform as financial analysis.
Also, on the website I can search 400,000+ companies, but there only seems to be ~4900 filings on the dashboard. Doesn't add up to me.
I would like to echo dougk7's comments regarding statistics for ML. The fundamentals of ML would be of great interest - moreso with ML becoming more en vouge.
The majority of my math education came from my sister who was a few years ahead of me in school. She'd learn w/e they were teaching, then would teach it to me at night. I'm forever in debt for this early education as it'd helped me in so many aspects of my life.
Statsmodels is a step in the right direction.