the availability of affordable compute power enabled by moore' law has been enabling rapid advances in machine learning solutions and driving adoption across diverse segments of the industry the ability to learn complex models underlying the real-world processes from observed (trainingdata through systemiceasy-to-apply machine learning solution stacks has been of tremendous attraction to businesses to harness meaningful business value the appeal and opportunities of machine learning have resulted in the availability of many resources--bookstutorialsonline trainingand courses for solution developersanalystsengineersand scientists to learn the algorithms and implement platforms and methodologies it is not uncommon for someone just starting out to get overwhelmed by the abundance of the material in additionnot following structured workflow might not yield consistent and relevant results with machine learning solutions key requirements for building robust machine learning applications and getting consistentactionable results involve investing significant time and effort in understanding the objectives and key value of the projectestablishing robust data pipelinesanalyzing and visualizing dataand feature engineeringselectionand modeling the iterative nature of these projects involves several select apply validate tune cycles before coming up with suitable machine learning-based model final and important step is to integrate the solution (machine learning modelinto existing (or neworganization systems or business processes to sustain actionable and relevant results hencethe broad requirements of the ingredients for robust machine learning solution require development platform that is suited not just for interactive modeling of machine learningbut also excels in data ingestionprocessingvisualizationsystems integrationand strong ecosystem support for runtime deployment and maintenance python is an excellent choice of language because it fits the need of the hour with its multi-purpose capabilitiesease of implementation and integrationactive developer communityand ever-growing machine learning ecosystemleading to its adoption for machine learning growing rapidly the authors of this book have leveraged their hands-on experience with solving real-world problems using python and its machine learning ecosystem to help the readers gain the solid knowledge needed to apply essential conceptsmethodologiestoolsand techniques for solving their own real-world problems and use-cases practical machine learning with python aims to cater to readers with varying skill levels ranging from beginners to experts and enable them in structuring and building practical machine learning solutions --ram varrasenior principal engineerintel xxiii