Machine Learning Algorithms divided into three categories:
1. Supervised Learning
How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
2. Unsupervised Learning
How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.
3. Reinforcement Learning
How it works: Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process
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When exploring the types of machine learning algorithms for your assignments, it’s important to understand their practical applications in real-world scenarios. Just like the steady improvement of the University of Sunderland ranking showcases its commitment to innovation and quality education, mastering algorithms such as supervised, unsupervised, and reinforcement learning can help you excel in your machine learning projects and academic tasks.
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