handling large data on single computer unpacked data setnumerous read and write operations ( /oare occurringbut the cpu remains largely idlewhereas with the compressed data set the cpu gets its fair share of the workload keep this in mind while we explore few solutions choosing the right algorithm choosing the right algorithm can solve more problems than adding more or better hardware an algorithm that' well suited for handling large data doesn' need to load the entire data set into memory to make predictions ideallythe algorithm also supports parallelized calculations in this section we'll dig into three types of algorithms that can do thatonline algorithmsblock algorithmsand mapreduce algorithmsas shown in figure online algorithms problems choose the right algorithms block matrices mapreduce solutions choose the right data structures choose the right tools handling large data general tips figure overview of techniques to adapt algorithms to large data sets online learning algorithms severalbut not allmachine learning algorithms can be trained using one observation at time instead of taking all the data into memory upon the arrival of new data pointthe model is trained and the observation can be forgottenits effect is now incorporated into the model' parameters for examplea model used to predict the weather can use different parameters (like atmospheric pressure or temperaturein different regions when the data from one region is loaded into the algorithmit forgets about this raw data and moves on to the next region this "use and forgetway of working is the perfect solution for the memory problem as single observation is unlikely to ever be big enough to fill up all the memory of modernday computer listing shows how to apply this principle to perceptron with online learning perceptron is one of the least complex machine learning algorithms used for binary classification ( or )for instancewill the customer buy or not