Feature Engineering Made Easy

Identify unique features from your dataset in order to build powerful machine learning

Sinan Ozdemir , Divya Susarla 지음

Packt(GCO Science) 2018년 01월 22일 출간

작품소개

책소개

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목차

1: INTRODUCTION TO FEATURE ENGINEERING
2: FEATURE UNDERSTANDING ？ WHAT'S IN MY DATASET?
3: FEATURE IMPROVEMENT - CLEANING DATASETS
4: FEATURE CONSTRUCTION
5: FEATURE SELECTION
6: FEATURE TRANSFORMATIONS
7: FEATURE LEARNING
8: CASE STUDIES

책 속으로

▶What this book covers
- Chapter 1, Introduction to Feature Engineering, is an introduction to the basic terminology of feature engineering and a quick look at the types of problems we will be solving throughout this book.
- Chapter 2, Feature Understanding ？. What's in My Dataset?, looks at the types of data we will encounter in the wild and how to deal with each one separately or together.
- Chapter 3, Feature Improvement - Cleaning Datasets, explains various ways to fill in missing data and how different techniques lead to different structural changes in data that may lead to poorer machine learning performance.
- Chapter 4, Feature Construction, is a look at how we can create new features based on what was already given to us in an effort to inflate the structure of data.
- Chapter 5, Feature Selection, shows quantitative measures to decide which features are worthy of being kept in our data pipeline.
- Chapter 6, Feature Transformations, uses advanced linear algebra and mathematical techniques to impose a rigid structure on data for the purpose of enhancing performance of our pipelines.
- Chapter 7, Feature Learning, covers the use of state-of-the-art machine learning and artificial intelligence learning algorithms to discover latent features of our data that few humans could fathom.
- Chapter 8, Case Studies, is an array of case studies shown in order to solidify the ideas of feature engineering.

출판사 서평

▶Editorial Review
This book will cover the topic of feature engineering. A huge part of the data science and machine learning pipeline, feature engineering includes the ability to identify, clean, construct, and discover new characteristics of data for the purpose of interpretation and predictive analysis.

In this book, we will be covering the entire process of feature engineering, from inspection to visualization, transformation, and beyond. We will be using both basic and advanced mathematical measures to transform our data into a form that's much more digestible by machines and machine learning pipelines.

By discovering and transforming, we, as data scientists, will be able to gain a whole new perspective on our data, enhancing not only our algorithms but also our insights.

인물정보

저자(글) Sinan Ozdemir

Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own startup, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.After completing a Fellowship at the Y Combinator accelerator, Sinan spent most of his time working on his fast-growing company, while creating educational material for data science.

저자(글) Divya Susarla

Divya Susarla is an experienced leader in data methods, implementing and applying tactics across a range of industries and fields including investment management, social enterprise consulting, and wine marketing. She trained in data by way of specializing in Economics and Political Science at University of California, Irvine, cultivating a passion for teaching by developing an analytically based, international affairs curriculum for students through the Global Connect program.Divya is currently focused on natural language processing and generation techniques at Kylie.ai, a startup helping clients automate their customer support conversations. When she is not busy working on building Kylie.ai and writing educational content, she spends her time traveling across the globe and experimenting with new recipes at her home in Berkeley, CA.