Types of ML models
Machine Learning, a sub-field of Artificial Intelligence (AI), has three types of algorithms:
- Supervised learning
- Unsupervised learning
- Reinforcement Learning
Supervised learning
In supervised learning, we are given a data set and already know what correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning is the type of learning that takes place when the training instances are labelled with the correct result, which gives feedback about how learning is progressing.
Supervised learning is a type of machine learning algorithm that uses a known (training) dataset to preform predictions.
Supervised learning algorithm seeks to build a model that can predict the response values for a (test) dataset.
Support Vector Machines (SVM)
Unsupervised learning
Unsupervised learning allows us to approach problems with little or no idea what results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.
Unsupervised learning is useful when you want to explore your data but don’t yet have a specific goal or are not sure what information the data contains. It’s also a good way to reduce the dimensions of your data.
In unsupervised learning, the goal is harder because there are no pre-determined categorizations.
If you are training machine learning model only with a set of inputs (and without providing any outputs), it is called unsupervised learning model, which will be able to find the structure or relationships between different inputs.
Reinforcement Learning
Reinforcement learning is a type of machine learning (ML) algorithm that allows the agent to decide the best next action based on its current state, by learning behaviours that will maximize the reward.
Reinforcement algorithms usually learn optimal actions through trial and error. The agent can then use rewards to understand the optimal state of game play and choose the next action.
