buildingtuningand deploying models very popular saying in the machine learning community is " of machine learning is data processingand going by the structure of this bookthe quote seems quite apt in the preceding you saw how you can extractprocessand transform data to convert it to form suitable for learning using machine learning algorithms this deals with the most important part of using that processed datato learn model that you can then use to solve real-world problems you also learned about the crisp-dm methodology for developing data solutions and projects--the step involving building and tuning these models is the final step in the iterative cycle of machine learning if you followed all the steps prescribed in the earlier by now you must have cleaned and processed data\feature set this data will mostly be numeric in the form of arrays or dataframes (feature setmost machine learning algorithms require the data to be in numeric format as at the heart of any machine learning algorithmwe have some mathematical equations and an optimization problem to either minimize error\loss or maximize profit hence machine learning algorithms always work on numeric data check out for feature engineering techniques to convert structured as well as unstructured data into ready-to-use numeric formats we start this by learning about different types of algorithms you can use then you will learn how to choose relevant algorithm for the data that you haveyou will then be introduced to the concept of hyperparameters and learn how to tune the hyperparameters of any algorithm the also covers novel approach to interpreting models using open source frameworks besides thisyou will also learn about persisting and deploying the developed models so you can start using them for your own needs and benefits based on the preceding topicsthe includes into the following five major sectionsbuilding models model evaluation techniques model tuning model interpretation deploying models in action you should be fully acquainted with the material of the earlier since it will help in better understanding of the various aspects of this all the code snippets and examples used in this are available in the github repository for this book at named model_build_tune_deploy py for all the examples used in this and try the examples as you read this or you can even refer to the jupyter notebook named buildingtuning and deploying models ipynb for more interactive experience (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python