K Means Clustering is an iterative algorithm that devides the unlabled dataset into k different clusters in such way that each dataset belongs to only one group that has similar properties.

Apriori algorithm uses frequent itemsets to generate association rules. It is based on the concept that a subset of a frequent itemset must also be frequent itemset

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.

There are multiple types of nueral networks ex. ANN, CNN, RNN, Feed Forward & Back Propagation.

Frequent Pattern Growth algorithm is a method of finding  frequent patterns without candidate set generation . It counstructs FP tree than using the generate and test strategy of apriori.

The goal of SVM is to create a best line or decision boundary that can segregate n-dimensional space into classes such that we can easily put the new data points in the correct category in the future.

A decision tree is a flowchart like structure where, each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label.

PCA is dimensionality reduction method that is often used to reduce the dimensionality of large data sets by transforming a large set of variables into a smaller one that still contains most of the information in the large set.

LDA is a dmensionality reduction technique used as a preprocessing step for pattern classification and machine learning applications .