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### Machine Learning Can Reach Heights With These Algorithms

- Supervised Machine Learning Algorithm
- Unsupervised Machine Learning Algorithms
- Reinforcement Learning

- P(h|d) = Posterior probability. The probability of hypothesis h being true, given the data d, where P(h|d)= P(d1| h) P(d2| h)....P(dn| h) P(d)
- P(d|h) = Likelihood. The probability of data d given that the hypothesis h was true.
- P(h) = Class prior probability. The probability of hypothesis h being true (irrespective of the data)
- P(d) = Predictor prior probability. Probability of the data (irrespective of the hypothesis)

- Association: To discover the probability of the co-occurrence of items in a collection. It is extensively used in market-basket analysis. Example: If a customer purchases bread, he is 80% likely to also purchase eggs.
- Clustering: To group samples such that objects within the same cluster are more similar to each other than to the objects from another cluster.
- Dimensionality Reduction: True to its name, Dimensionality Reduction means reducing the number of variables of a dataset while ensuring that important information is still conveyed. Dimensionality Reduction can be done using Feature Extraction methods and Feature Selection methods. Feature Selection selects a subset of the original variables. Feature Extraction performs data transformation from a high-dimensional space to a low-dimensional space. Example: PCA algorithm is a Feature Extraction approach.