To start let's talk about the relationship between artificial intelligence for AI and machine learning. AI refers to the broad capability of machines to perform activities using human-like intelligence, Machine Learning or short-form ML is a type of artificial intelligence it allows computers to automatically learn and improve from experience without being explicitly programmed to do so. Using machine learning computers can learn from data to discover patterns and make predictions supervised learning is a type of machine learning technique in which every training sample from the data set has a corresponding label or output value associated with it as a result, the algorithm learns to predict labels or output values you can use supervised learning to do things like predict a sale price of a house or classify object in an image.
There are no labels for the training data the algorithm tries to learn the underlying patterns or distributions that govern the data you will discover more about this technique later in this lesson remember in supervised and unsupervised learning models inspect data to discover patterns then humans use the patterns learned by the model to gain new understandings or make predictions there is another type of machine learning called reinforcement learning which takes a different approach. Reinforcement learning is learning what actions to take in a situation to maximize reward it is similar to how you might train your pet if your dog does something you wanted to do you might reward it with a treat if it does something you don't want it to do you might correct it with a small penalty like raising your voice just a little bit your dog learns to do the things that get a treat and avoid doing things that get a correction in machine learning.
Reinforcement learning works exactly like this now let's see how machine learning helps solve problems and how this differs from traditional problem solving. In traditional problem solving with software, a person analyzes a problem and engineers the solution in code to solve that problem for many real world examples.
This process takes a lot of time it might even be impossible this is because the correct solution needs to consider numerous edge cases for example imagine the challenging task of writing a program that can detect if a cat is present in an image or not traditional problem solving would require careful attention to details like varying lighting conditions different type of scats colors etc. In machine learning we have a flexible component called the model we also have a special program called the model training algorithm to adjust the model to real world data the result is a trained model which can be used to predict outcomes which are not part of the data set used to train it. In a way machine learning automates some of the statistical reasoning and pattern matching that the problem solver would traditionally do the flexibility of the model is the key here the machine learning field has seen rapid and recent growth as you start on your machine learning journey you might see related but different definitions of the terms we will use in this lesson this is because machine learning is a new field at the intersection of statistics applied math and computer science each of these fields might have a slightly different formal definition for the same terms.
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