Supervised and Unsupervised Machine Learning Models
1. Supervised and Unsupervised Machine Learning Models
so what do these machine learning models look like two of the most common classes of machine learning models are unsupervised and supervised ml models the key difference between the two is that with supervised models we have labels labeled data is data that comes with a tag like a name a type or a number unlabeled data is data that comes with no tag so what can you do with supervised and unsupervised models this graph is an example of the sort of problem a supervised model might try to solve for example let's say you're the owner of a restaurant what type of food do they serve let's say pizza or dumplings no let's say pizza I like pizza anyway you have historical data of the bill amount and how much different people tip based on the order type pickup or delivery in supervised learning the model learns from past examples to predict future values here the model uses a total bill amount data to predict the future tip amount based on whether an order was picked up or delivered also people tip your delivery drivers they work really hard this is an example of the sort of problem that an unsupervised model might try to solve here you want to look at tenure and income and then group or cluster employees to see whether someone is on the fast trck nice work blue shirt unsupervised problems are all about discovery about looking at the raw data and seeing if it naturally falls into groups this is a good start but let's go a little deeper to show this difference graphically because understanding these Concepts is the foundation for your understanding of generative AI in supervised learning testing data values X are input into the model the model outputs a prediction and Compares it to the training data used to train the model if the predicted test data values and actual training data values are far apart that is called error the model tries to reduce this error until the predicted and actual values are closer together this is a classic optimization problem so let's check in so far we've explored differences between artificial intelligence and machine learning and supervised and unsupervised learning that's a good start but what's next let's briefly explore where deep learning fits as a subset of machine learning methods and then I promise we'll start talking about gen2. Let's practice!
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