Essentially what I would love to do is create an A.I. app that will be fed the same data that the "experts" had and see if I can create something more accurate and beat them at it. Is this a viable approach?
Sure, you can use one or more supervised learning techniques to train a model here. You have features, a target variable and ground truth for that variable.
In addition to applying ML you have learned, all you need to do to test your application fairly is reserve some of the data you have with expert predictions for comparison as test data (i.e. do not train using it).
I would caveat that with some additional thoughts:
You haven't really outlined an "approach" here, other than mentioning use of ML.
Be careful not to leak future data back into the predictive model when building a test version.
Predicting stock and markets is hard, because they react to their own predictability and many professional organisations trade on the slightest advantage they can calculate, with experienced and highly competent staff both gathering and analysing data.
Not directly part of the answer, but to anyone just starting out and discovering machine learning, and finding this Q&A:
Please don't imagine rich rewards from predicting markets using stats at home, it doesn't happen. If you think that this is a route to "beating the market" be aware that you are far from the first to think of doing this, and such a plan can be summarised like this:
- Market Data + ML
You can fill in the ??? by learning loads about financial markets - i.e. essentially by becoming one of the experts. ML is not a short-cut, but it might be a useful tool if you are, or plan to be, a market analyst.