unsupervised machine learning algorithms as the name suggeststhese kinds of machine learning algorithms do not have any supervisor to provide any sort of guidance that is why unsupervised machine learning algorithms are closely aligned with what some call true artificial intelligence it can be understood as followssuppose we have input variable xthen there will be no corresponding output variables as there is in supervised learning algorithms in simple wordswe can say that in unsupervised learning there will be no correct answer and no teacher for the guidance algorithms help to discover interesting patterns in data unsupervised learning problems can be divided into the following two kinds of problemclusteringin clustering problemswe need to discover the inherent groupings in the data for examplegrouping customers by their purchasing behavior associationa problem is called association problem because such kinds of problem require discovering the rules that describe large portions of our data for examplefinding the customers who buy both and -means for clusteringapriori algorithm for association are the examples of unsupervised machine learning algorithms reinforcement machine learning algorithms these kinds of machine learning algorithms are used very less these algorithms train the systems to make specific decisions basicallythe machine is exposed to an environment where it trains itself continually using the trial and error method these algorithms learn from past experience and tries to capture the best possible knowledge to make accurate decisions markov decision process is an example of reinforcement machine learning algorithms most common machine learning algorithms in this sectionwe will learn about the most common machine learning algorithms the algorithms are described belowlinear regression it is one of the most well-known algorithms in statistics and machine learning basic conceptmainly linear regression is linear model that assumes linear relationship between the input variables say and the single output variable say in other wordswe can say that can be calculated from linear combination of the input variables the relationship between variables can be established by fitting best line types of linear regression linear regression is of the following two typessimple linear regressiona linear regression algorithm is called simple linear regression if it is having only one independent variable