Machine Learning Process
Machine learning (ML) lies at the heart of data science. It is an umbrella term for a huge set of algorithms that find and model patterns in data. These algorithms can be broken down into various categories, such as supervised, unsupervised, and reinforcement learning.
In supervised problems, we have access to a historical view of labeled records and fit models to predict them—for example, blood test data that's been labeled with the test result. In unsupervised problems, there is no such data available, and labels may need to be created using clustering techniques. In later sections, we will break these down in more detail and work with examples of each.
Reinforcement learning is concerned with maximizing a reward function through an iterative process, such as a simulation. Similar to the other types of learning algorithms, there's a wide range of problems that reinforcement learning can be applied to, such as teaching a robot how to...
