Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems
About this ebook
Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.
You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data.
By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.
What you will learnIdentify and leverage different feature typesClean features in data to improve predictive powerUnderstand why and how to perform feature selection, and model error analysisLeverage domain knowledge to construct new featuresDeliver features based on mathematical insightsUse machine-learning algorithms to construct featuresMaster feature engineering and optimizationHarness feature engineering for real world applications through a structured case studyWho this book is forIf you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.
