When to do machine learning
I realized that before starting on technical details of methods I should write some about when to use machine learning, or when it is intelligent to use artificial intelligence ? In fact, in most of the cases avoid machine learning as much as possible. In first place, you use machine learning when your measurements, sensor values, data is not sufficiently descriptive to get final result that you are looking for. For example, covid tests works with very high precision currently. If your input is the result of the test you don't need machine learning. However, if you are detecting covid from chest x-ray you have many small tiny pixel transparancy measurements thus you need some kind of learning. In this case, you should ask while the alternative way is providing good quality result do we really need this learning ? In summary you should try to get descriptive data as possible in the first place. Later, if you have only small amount of samples, the best you can do is use them as te...