able to solve your problem examples of supervised machine learning tasks includeidentifying the zip code from handwritten digits on an envelope here the input is scan of the handwritingand the desired output is the actual digits in the zip code to create dataset for building machine learning modelyou need to collect many envelopes then you can read the zip codes yourself and store the digits as your desired outcomes determining whether tumor is benign based on medical image here the input is the imageand the output is whether the tumor is benign to create dataset for building modelyou need database of medical images you also need an expert opinionso doctor needs to look at all of the images and decide which tumors are benign and which are not it might even be necessary to do additional diagnosis beyond the content of the image to determine whether the tumor in the image is cancerous or not detecting fraudulent activity in credit card transactions here the input is record of the credit card transactionand the output is whether it is likely to be fraudulent or not assuming that you are the entity distributing the credit cardscollecting dataset means storing all transactions and recording if user reports any transaction as fraudulent an interesting thing to note about these examples is that although the inputs and outputs look fairly straightforwardthe data collection process for these three tasks is vastly different while reading envelopes is laboriousit is easy and cheap obtaining medical imaging and diagnoseson the other handrequires not only expensive machinery but also rare and expensive expert knowledgenot to mention the ethical concerns and privacy issues in the example of detecting credit card frauddata collection is much simpler your customers will provide you with the desired outputas they will report fraud all you have to do to obtain the input/output pairs of fraudulent and nonfraudulent activity is wait unsupervised algorithms are the other type of algorithm that we will cover in this book in unsupervised learningonly the input data is knownand no known output data is given to the algorithm while there are many successful applications of these methodsthey are usually harder to understand and evaluate examples of unsupervised learning includeidentifying topics in set of blog posts if you have large collection of text datayou might want to summarize it and find prevalent themes in it you might not know beforehand what these topics areor how many topics there might be thereforethere are no known outputs why machine learning