From the course: Applied Machine Learning: Foundations

Overview of types of machine learning

From the course: Applied Machine Learning: Foundations

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Overview of types of machine learning

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Machine learning is a vast field offering a variety of approaches depending on the nature of the problem you're solving and the type of data you're dealing with. At its core, machine learning can be categorized into three main types: Supervised learning, unsupervised learning, and reinforcement learning. Let's explore each type and where they're most effectively applied. Supervised learning involves training a model on a labeled data set, meaning each training example is paired with the correct output. The goal is to create a model to learn to predict the output from the input data, making it ideal for tasks like classification and regression. Classification means we will predict a label like has cancer, is spam, will default on loan, or will complete a course. Regression tasks predict a numeric value. Examples include predicting blood pressure, cells, or points scored. This course will walk through a regression example. On the other hand, unsupervised learning deals with data that doesn't have labeled responses. Here, the model tries to learn patterns and structure from the data itself. It's great for discovery, identifying hidden patterns or grouping similar data points together in clustering tasks. I love to use these techniques to explore data. Lastly, there's reinforcement learning, an approach where an agent learns to make decisions by performing actions in an environment to achieve a goal. It learns from trial and error, receiving rewards or penalties for actions it takes. This type of learning is powerful in scenarios requiring a sequence of decisions, such as navigating a maze or optimizing investment strategies. Each type of machine learning has its own set of tools, techniques and applications, making the field rich with possibilities. Whether you're automating decision-making processes, uncovering patterns in data, or training a robot to improve through experience, understanding these foundational concepts is your first step in mastering machine learning. In this course, we'll explore supervised learning in more detail with a focus on regression.

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