Hy, I wanna learn about what is Machine Learning.
1. What are its inputs ( what do we give it)
2. Outputs ( what do we get from it)
3. What can we do with in field of cows and cow farming.
Thanksss
In: 4
The current wave of machine learning can be boiled down to pattern matching. You have a set of starting inputs and a bunch of outcomes. You use the ML algorithms to match a bunch of starting states into potential outputs. It basically gives you a % chance that your outcome is going to be something.
One example may be to see when cows produce the best milk. So maybe you’re looking for a certain mix of nutrients in the milk or maybe volume. You start gathering data about the cows. It can be when they eat, what they eat, what other cows they interact with. The color of the cow, spots on the cow, etc. You can pick pretty much anything you want to feed it into the algorithm.
The more data you have the better. Maybe it turns out cows produce the most milk after eating after 3pm. Maybe for some reason all cows with 3 spots produce the best milk. You don’t really know, all you know is that for some reason all of these things impact your outcome. So going forward maybe you adjust the feeding schedule or only get cows with 3 spots.
Word of caution – this is where correlation does not mean causation comes into play. ML basically just looks for correlation, not causation. Not to say its worthless. The idea is that the more data and more data points you have, the less likely your algorithm will be biased (Even then there can be unconscious biases) and less the correlation/causation relationship matters. And in some cases, that’s good enough.
Latest Answers