Introduction
Machine learning is a type of artificial intelligence that allows computers to learn from experience without being explicitly programmed. It is used for a variety of applications in data mining, predictive analytics, and many other fields.
Supervised Machine Learning
Supervised machine learning is a type of machine learning where the training data is labeled. It’s supervised because you have to tell the algorithm what its output should look like, otherwise it won’t know how to fit its model.
It’s also helpful to think about supervised as opposed to unsupervised when it comes to understanding how algorithms work: Unsupervised algorithms are more exploratory in nature, whereas supervised algorithms tend to follow more specific paths toward their goal (producing an accurate prediction).
Unsupervised Machine Learning
Unsupervised machine learning is a machine learning task where the learner tries to find hidden structure in unlabeled data. Unsupervised machine learning has many applications, such as clustering and dimensionality reduction. It is often contrasted with supervised machine learning (when you have both labeled and unlabeled data) because it does not require human intervention or feedback on training examples during runtime.
Reinforcement Machine Learning
Reinforcement learning is a subfield of machine learning that provides an alternative to the more commonly used supervised and unsupervised learning methods. In reinforcement learning, an agent interacts with an environment in order to maximize its reward.
The agent performs actions that affect its environment and receives feedback on whether those actions were good or bad based on their consequences (reward). The goal of reinforcement learning is for an agent to figure out which actions lead to better outcomes so it can maximize rewards over time.
Semi-supervised Machine Learning
Semi-supervised learning is a machine learning technique that uses both labeled and unlabeled data. It is a way to use unlabeled data to learn the structure of the unlabeled data. With semi-supervised learning, you have some labeled examples and some unannotated examples. You can then use these two types of datasets together and train an algorithm using them both so that it can predict values for new examples without having been trained on them before (the “unlabeled” ones).
Semi-supervised methods have been used successfully in many domains including:
- Speech recognition – The task here was to build systems capable of recognizing spoken words with high accuracy despite being trained only on examples containing full transcriptions (labelled) or only partially transcribed speech signals (unlabelled).
Different types of machine learning
There are four main types of machine learning: supervised, unsupervised, reinforcement and semi-supervised.
Supervised Machine Learning is used to train a model using labeled training data. The model learns what the correct output should be for each input using this labeled data set. Once the model has been trained it can then be used to make predictions on new observations that have not yet been seen during training (test data). Supervised Machine Learning algorithms include decision trees, logistic regression and support vector machines (SVMs).
Unsupervised Machine Learning algorithms are those which do not require any sort of target variable or label information in order to make predictions about other variables in your dataset; instead these algorithms learn about correlations between different variables within an unlabeled dataset as opposed to being “taught” how certain things should behave based on their relationship with another thing like they would be with supervised learning methods such as clustering or classification where both class labels must always exist together so they can compare their relative distance from each other before deciding which one belongs together more closely than others do…
Conclusion
We have looked at different types of machine learning and their applications. We hope this article has been helpful to you in understanding the basics of each type and how they work. If there is anything else we missed out on please feel free to comment below!
