Practical machine learning with PyTorch

Abstract

Money makes the world go round and in the current ecosystem of data intensive business practices, it is safe to claim that data also makes the world go round. A very important skill set for data scientists is to match the technical aspects of analytics with its business value, i.e., its monetary value. This can be done in a variety of ways and is very much dependent on the type of business and the data available. In the earlier chapters, we covered problems that can be framed as business problems (leveraging the CRISP-DM model) and linked to revenue generation. In this chapter we will directly focus on two very important problems that can directly have a positive impact on the revenue streams of businesses and establishments particularly from the retail domain. This chapter is also unique in the way that we address a different paradigm of Machine Learning algorithm altogether, focusing more on tasks pertaining to pattern recognition and unsupervised learning.