denoted by vector (python listsuch that each element in the vector is for specific data feature or attribute we discuss more about features and data points in detail in future section as well as in "feature engineering and selectioncoming back to the typical tasks that could be classified as machine learning tasksthe following list describes some popular tasks classification or categorizationthis typically encompasses the list of problems or tasks where the machine has to take in data points or samples and assign specific class or category to each sample simple example would be classifying animal images into dogscatsand zebras regressionthese types of tasks usually involve performing prediction such that real numerical value is the output instead of class or category for an input data point the best way to understand regression task would be to take the case of real-world problem of predicting housing prices considering the plot areanumber of floorsbathroomsbedroomsand kitchen as input attributes for each data point anomaly detectionthese tasks involve the machine going over event logstransaction logsand other data points such that it can find anomalous or unusual patterns or events that are different from the normal behavior examples for this include trying to find denial of service attacks from logsindications of fraudand so on structured annotationthis usually involves performing some analysis on input data points and adding structured metadata as annotations to the original data that depict extra information and relationships among the data elements simple examples would be annotating text with their parts of speechnamed entitiesgrammarand sentiment annotations can also be done for images like assigning specific categories to image pixelsannotate specific areas of images based on their typelocationand so on translationautomated machine translation tasks are typically of the nature such that if you have input data samples belonging to specific languageyou translate it into output having another desired language natural language based translation is definitely huge area dealing with lot of text data clustering or groupingclusters or groups are usually formed from input data samples by making the machine learn or observe inherent latent patternsrelationships and similarities among the input data points themselves usually there is lack of pre-labeled or pre-annotated data for these tasks hence they form part of unsupervised machine learning (which we will discuss later onexamples would be grouping similar productsevents and entities transcriptionsthese tasks usually entail various representations of data that are usually continuous and unstructured and converting them into more structured and discrete data elements examples include speech to textoptical character recognitionimages to textand so on this should give you good idea of typical tasks that are often solved using machine learningbut this list is definitely not an exhaustive one as the limits of tasks are indeed endless and more are being discovered with extensive research over time