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Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems
A perfect guide to speed up the predicting power of machine learning algorithms
Key FeaturesDesign, discover, and create dynamic, efficient features for your machine learning applicationUnderstand your data in-depth and derive astonishing data insights with the help of this GuideGrasp powerful feature-engineering techniques and build machine learning systemsBook DescriptionFeature 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.
Table of ContentsIntroduction to Feature EngineeringFeature Understanding - What’s in My Data?Feature Improvement - Cleaning Datasets Feature ConstructionFeature SelectionFeature TransformationsAutomatic Construction of FeaturesCase Studies
Key FeaturesDesign, discover, and create dynamic, efficient features for your machine learning applicationUnderstand your data in-depth and derive astonishing data insights with the help of this GuideGrasp powerful feature-engineering techniques and build machine learning systemsBook DescriptionFeature 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.
Table of ContentsIntroduction to Feature EngineeringFeature Understanding - What’s in My Data?Feature Improvement - Cleaning Datasets Feature ConstructionFeature SelectionFeature TransformationsAutomatic Construction of FeaturesCase Studies
- GenresProgramming
318 pages, Kindle Edition
Published January 22, 2018
22 people are currently reading
59 people want to read
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December 17, 2018
More like "Guide to preprocessing data using scikit-learn". Good code examples, not so good algorithm explanations
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