methods based on the amount of human supervision in the learning process supervised learning unsupervised learning semi-supervised learning reinforcement learning methods based on the ability to learn from incremental data samples batch learning online learning methods based on their approach to generalization from data samples instance based learning model based learning we briefly cover the various types of learning methods in the following sections to build good foundation with regard to machine learning methods and the type of tasks they usually solve this should give you enough knowledge to start understanding which methods should be applied in what scenarios when we tackle various real-world use cases and problems in the subsequent of the book #discussing mathematical details and internals of each and every machine learning algorithm would be out of the current scope and intent of the booksince the focus is more on solving real-world problems by applying machine learning and not on theoretical machine learning hence you are encouraged to refer to standard machine learning references like pattern recognition and machine learningchristopher bishop and the elements of statistical learningrobert tibshirani et al for more theoretical and mathematical details on the internals of machine learning algorithms and methods supervised learning supervised learning methods or algorithms include learning algorithms that take in data samples (known as training dataand associated outputs (known as labels or responseswith each data sample during the model training process the main objective is to learn mapping or association between input data samples and their corresponding outputs based on multiple training data instances this learned knowledge can then be used in the future to predict an output yfor any new input data sample xwhich was previously unknown or unseen during the model training process these methods are termed as supervised because the model learns on data samples where the desired output responses/labels are already known beforehand in the training phase supervised learning basically tries to model the relationship between the inputs and their corresponding outputs from the training data so that we would be able to predict output responses for new data inputs based on the knowledge it gained earlier with regard to relationships and mappings between the inputs and their target outputs this is precisely why supervised learning methods are extensively used in predictive analytics where the main objective is to predict some response for some input data that' typically fed into trained supervised ml model supervised learning methods are of two major classes based on the type of ml tasks they aim to solve