classification the key objective of classification-based tasks is to predict categorial output labels or responses for the given input data the output will be based on what the model has learned in training phase as we know that the categorial output responses means unordered and discrete valueshence each output response will belong to specific class or category we will discuss classification and associated algorithms in detail in the upcoming also regression the key objective of regression-based tasks is to predict output labels or responses which are continues numeric valuesfor the given input data the output will be based on what the model has learned in its training phase basicallyregression models use the input data features (independent variablesand their corresponding continuous numeric output values (dependent or outcome variablesto learn specific association between inputs and corresponding outputs we will discuss regression and associated algorithms in detail in further also unsupervised learning as the name suggestsit is opposite to supervised ml methods or algorithms which means in unsupervised machine learning algorithms we do not have any supervisor to provide any sort of guidance unsupervised learning algorithms are handy in the scenario in which we do not have the libertylike in supervised learning algorithmsof having pre-labeled training data and we want to extract useful pattern from input data for exampleit can be understood as followssuppose we havexinput variablesthen there would be no corresponding output variable and the algorithms need to discover the interesting pattern in data for learning examples of unsupervised machine learning algorithms includes -means clusteringknearest neighbors etc based on the ml tasksunsupervised learning algorithms can be divided into following broad classesclustering association dimensionality reduction clustering clustering methods are one of the most useful unsupervised ml methods these algorithms used to find similarity as well as relationship patterns among data samples and then cluster those samples into groups having similarity based on features the real-world example of clustering is to group the customers by their purchasing behavior association another useful unsupervised ml method is association which is used to analyze large dataset to find patterns which further represents the interesting relationships between various items it is also termed as association rule mining or market basket analysis which is mainly used to analyze customer shopping patterns