get into wine tasting programming, you should know that there are many algorithms for machine learning. These algorithms can be used in different scenarios and for different purposes. ML.NET supports several algorithms, so it is important to choose the right one for your project. Since I do not know all the algorithms and Microsoft will likely continue to add new ones, I describe the most commonly used ones in this article:
Classification: define a category for items based on one or more input variables. In the case of binary classification, there are only two categories: true (1) or false (0). This is used for decision models, such as: is this a good or bad wine. In addition, there is multi-class classification, supporting more than two groups, such as: which region does this wine come from?
Clustering : divide items into groups, based on their properties. The best known way of klustering, is the K-Means algorithm. Example: what is the price range of this wine?
Recommendation: to make recommendations based on a user’s previous choices. This could be interesting if you are going to sell wine. If you know what wine a customer has bought before, you can recommend other wines based on buying behavior of other customers.
Transfer learning: use someone else’s model. Recognizing objects on images with machine learning, requires a huge amount of training data and hours of GPU time to process it. It would be wasted effort to create such a library when others have already done so. Microsoft recommends using TensorFlow in such cases.
Regression: predict values based on one or more properties. To predict these values, a model is trained based on historical data. A typical scenario in which regression is used is to determine prices. Although it is also a good candidate for … predicting the quality of wine!