[nanoGeek]

Machine learning is a hot topic today, like AI in general.

Facebook and Google use Machine learning algorithms to increase Ad revenue and serve more relevant information to the user.

Machine learning is also used in health care to predict Emergency Room wait times for example.

Many banks and big online retailers use Machine learning for fraud detection.

And of course Machine Learning is used in the stock market.

[curiously]

this is a very broad, top down look at the problems that are being solved but I want to know a bottom up, an specific detail or the problems that is good to tackle.

>ML in stock market

I know this is used here but what exactly? Mine tweets to predict stock prices with a contrarian strategy? Computer vision on stock charts to apply technical analysis trading system? Mine historical volatility of options on futures?

It would be great if there was some simple business or SaaS I can execute using machine learning.

But say you have a website now with 'We use ML to solve X". Then the next bigger challenge, convincing a business to fork over their data for the sake of machine learning without previous experience. Or do you have to work for free in the beginning to build credentials? 'Hey if you hand over your patient data at your ER, we'll figure the waiting time out, never done this before with another ER so we'll do it for free?'

I guess I'm interested in the applying entrepreneurial and business approach to solve an actual real world problem that is in demand using ML.

[ci5er]

Anything that is manual, tedious and error-prone. Or requires quick reaction time.

A "silly" example might be clinical diagnostic decision support. Instead of charts with history, lab results and meds to visually wade through and diagnose, there could be a simple UI. "Simple" means simply that instead of potentially 1000 fields that might be read or selectively updated, there would be 5 pre-selected by the computer. This could then go from medical unstructured "narrative" to automated ICD-10 coding. There would be lots of NLP, semantic analysis of large corpii, formal ontologies and RDF-encoded KB tangles to work though.

> Computer vision on stock charts to apply technical analysis trading system?

Why would you use computer vision? Just get a DVD full of the intra-day tick data for the instruments of interest going back 20-or-so years and find correlations.

And, I'm sure you know this, but a reader wandering through here may not: many of the kaggle competitions can be solved with ML techniques and many-or-most of the solution types would be valuable to the market or to large (hopefully funded) research problems. https://www.kaggle.com/