learning (supervisedalgorithm and training data features and corresponding labels this model will take features from new data samples and output predicted labels in the prediction phase unsupervised machine learning pipeline unsupervised machine learning is all about extracting patternsrelationshipsassociationsand clusters from data the processes related to feature engineeringscaling and selection are similar to supervised learning however there is no concept of pre-labeled data here hence the unsupervised machine learning pipeline would be slightly different in contrast to the supervised pipeline figure - depicts standard unsupervised machine learning pipeline figure - unsupervised machine learning pipeline figure - clearly depicts that no supervised labeled data is used for training the model with the absence of labelswe just have training data that goes through the same data preparation phase as in the supervised learning pipeline and we build our unsupervised model with an unsupervised machine learning algorithm and training features in the prediction phasewe extract features from new data samples and pass them through the model which gives relevant results according to the type of machine learning task we are trying to performwhich can be clusteringpattern detectionassociation rulesor dimensionality reduction real-world case studypredicting student grant recommendations let' take step back from what we have learned so farthe main objective here was to gain solid grasp over the entire machine learning landscapeunderstand crucial conceptsbuild on the basic foundationsand understand how to execute machine learning projects with the help of machine learning pipelines with the crisp-dm process model being the source of all inspiration let' put all this together to take very basic real-world case study by building supervised machine learning pipeline on toy dataset our major objective is as follows given that you have several students with multiple attributes like gradesperformanceand scorescan you build model based on past historical data to predict the chance of the student getting recommendation grant for research projectthis will be quick walkthrough with the main intent of depicting how to build and deploy real-world machine learning pipeline and perform predictions this will also give you good hands-on experience to get started with machine learning do not worry too much if you don' understand the details of each and every line of codethe subsequent cover all the toolstechniquesand frameworks used here in