Introduction
Machine learning is one of the hottest topics in tech, but it can be difficult to understand. Here’s a quick guide to the different types of machine learning and how they work.
Supervised Learning
Supervised learning is a type of machine learning where the algorithm learns from labeled data. It can be used for classification or regression, and it’s the most common type of machine learning (and therefore well-studied).
Supervised Learning Example: You want to use your sensor data to predict whether a patient has diabetes or not. To do this, you first collect some training samples by measuring glucose levels in patients with diabetes and those without it; then, using those measurements as inputs for your model along with their diagnosis (diabetes or not), you train a classifier that predicts whether future patients have diabetes based on their glucose levels alone.
Unsupervised Learning
Unsupervised learning is the process of discovering hidden patterns and relationships in data. It’s used for pattern recognition, classification, clustering and dimensionality reduction. For example: k-means clustering is an unsupervised learning algorithm that can be used for finding groups within your dataset based on similarity between them
Reinforcement Learning
Reinforcement learning is a technique that allows an agent to learn from interactions with the environment. It’s the most general form of machine learning, and can be applied to many different types of problems.
In reinforcement learning, we want our agent (a robot or other digital system) to maximize its reward by doing some task in an environment. The task itself doesn’t matter–it could be anything from playing chess against another player all the way down to picking up trash on your street corner! The only thing that matters here is that there exists some reward function which maps states into numbers representing their goodness or badness for achieving your goal: if you’re trying to win at chess and you see that one move gives you 10 points while another gives only 5 points then clearly it would be better for your overall score if you made that first choice next time around!
Bayesian Machine Learning
Bayesian Machine Learning is based on Bayesian inference, which allows you to calculate the probability of an event given a set of observed data. This can be used to make predictions about the future based on past data.
Bayesian inference works by using your prior knowledge about something (the prior) and updating it with new information (the likelihood). For example, if you know that 60{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} of people prefer chocolate ice cream over vanilla ice cream, then this is your prior belief; however, if someone tries both flavors and they like chocolate more than vanilla then this would change or update your belief–you now know that 80{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} prefer chocolate over vanilla!
Case-Based Reasoning Machine Learning
Case-based reasoning is a type of machine learning that uses past experiences to solve a new problem. It’s been used in many different industries, including healthcare and law.
Case-based reasoning is a data-driven approach to problem solving, where the system uses a set of cases to solve a new problem. The idea behind this is simple: If you have already solved similar problems before (or “cases”), then why not use those solutions as inspiration for solving this one?
For example, if you are trying to diagnose an illness by asking patients questions about symptoms they’ve experienced before–and then comparing them with known diseases–that would be considered case-based reasoning because it relies on previous cases involving similar symptoms in order for doctors or other medical professionals making decisions about treatment options based off those findings alone without having any prior knowledge beforehand
Machine learning can be a complicated topic, but it is worth understanding the different types.
Machine learning is a branch of artificial intelligence, and it’s all about automating the process of finding patterns in data. There are many different types of machine learning, each with its own advantages and disadvantages. Let’s take a closer look at some of these types:
- Supervised Learning
In supervised learning, you have both the inputs (the data) and the desired outputs (the target values). Your goal is to train an algorithm so that it can predict those outputs given new inputs–for example, if you want to build an app that can tell whether someone has diabetes based on their age, weight and blood pressure readings; this would be considered supervised because there are known outcomes for these variables which we want our model to learn from them without having any additional information about whether someone has diabetes or not.* Unsupervised LearningUnsupervised learning refers to when we don’t have any targets for our predictions but still want our model do something useful with its predictions anyway.* Reinforcement LearningReinforcement Learning refers specifically when an agent interacts with its environment by taking actions based upon their consequences until some end state is reached
Conclusion
Machine learning is a complicated topic, but it’s worth understanding the different types. It can be helpful to understand what type of machine learning algorithm might be best suited for your problem and its limitations.
