Machine learning has become an integral part of many commercial applications & research projects but this field is not exclusive to large companies with extensive research teams If you use Python even as a beginner this book will teach you practical ways to build your own machine learning solutions With all the data available today machine learning applications are limited only by your imagination You'll learn the steps necessary to create a successful machine-learning application with Python & the scikit-learn library Authors Andreas Muller & Sarah Guido focus on the practical aspects of using machine learning algorithms rather than the math behind them Familiarity with the Num Py & matplotlib libraries will help you get even more from this book With this book you'll learn Fundamental concepts & applications of machine learning Advantages & shortcomings of widely used machine learning algorithms How to represent data processed by machine learning including which data aspects to focus on Advanced methods for model evaluation & parameter tuning The concept of pipelines for chaining models & encapsulating your workflow Methods for working with text data including text-specific processing techniques Suggestions for improving your machine learning & data science skills