ix the modeling process engineering features and selecting model training your model validating model predicting new observations types of machine learning supervised learning semi-supervised learning summary unsupervised learning handling large data on single computer the problems you face when handling large data general techniques for handling large volumes of data choosing the right algorithm choosing the right data structure selecting the right tools general programming tips for dealing with large data sets don' reinvent the wheel get the most out of your hardware reduce your computing needs case study predicting malicious urls step defining the research goal step acquiring the url data step data exploration step model building case study building recommender system inside database tools and techniques needed step research question step data preparation step model building step presentation and automation summary first steps in big data distributing data storage and processing with frameworks hadoopa framework for storing and processing large data sets sparkreplacing mapreduce for better performance