Clover collects tons of data about its patients, probably more than most health plans. They may only have 19,000 patients, but they also like to talk about how their data is very wide. Most health plans I've worked with do a terrible job of even collecting the simplest types of data and outsource a vast majority of their data collection processes. These health plans have aging technology and a reluctance to use new and open source tools. A great example of this is how many health plans manage to somehow overpay claims to the tune of 10-100s of millions of dollars per year, and have no idea why. There's an entire cottage industry devoted to solving this problem for insurers.
Most of the tests you mention above are wasteful for your typical Medicare Advantage enrollee. There's a ton of low hanging fruit for a start-up like Clover to make a meaningful impact. Trying to change human behavior through diet or exercise is incredibly difficult, especially for those from disadvantaged communities or of lower socioeconomic status. Kudos to Clover to trying to make a marginal impact on that front; most insurance plans wouldn't do anything.
It depends on what your goals are. If you'd like to become an ML Engineer or Data Scientist, Tensorflow should be last thing you learn. First, develop a solid foundation in linear algebra and statistics. Then, familiarize yourself with a nice ML toolkit like Scikit-Learn and The Elements of Statistical Learning (which is free online). The rest is a distraction.
In addition to the linear algebra and statistics MOOCS mentioned, I'll also add:
Virtually all data used to predict crimes or recidivism is fraught with human bias, for example. Not sure that we want to reproduce the bias of criminal justice system in any prediction problem involving this type of data.
> If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases.
This is rarely the case when working wtih real data, and thus inspecting whether our models are biased against protected classes is probably one of the most important things an ML practitioner should do.
Most of the tests you mention above are wasteful for your typical Medicare Advantage enrollee. There's a ton of low hanging fruit for a start-up like Clover to make a meaningful impact. Trying to change human behavior through diet or exercise is incredibly difficult, especially for those from disadvantaged communities or of lower socioeconomic status. Kudos to Clover to trying to make a marginal impact on that front; most insurance plans wouldn't do anything.