Название: Feature Engineering for Modern Machine Learning with Scikit-Learn: Advanced Data Science and Practical Applications
Автор: Cuantum Technologies
Издательство: Cuantum Technologies
Серия: Advanced Data Analysis Series
Год: November 6, 2024
Страниц: 506
Язык: английский
Формат: pdf, epub
Размер: 13.2 MB

Unleash the Power of Feature Engineering for Cutting-Edge Machine Learning.

Transform raw data into powerful features with Feature Engineering for Modern Machine Learning with Scikit-Learn: Advanced Data Science and Practical Applications. This essential guide takes you beyond the basics, teaching you how to create, optimize, and automate features that elevate Machine Learning (ML) models. With a focus on real-world applications and advanced techniques, this book equips data scientists, Machine Learning engineers, and analytics professionals with the skills to make impactful, data-driven decisions.

This book aims to provide a practical and hands-on approach to starting with Mastering the Creative Power of AI. Whether you are a beginner without programming experience or an experienced programmer looking to expand your skills, this book is designed to help you develop your skills and build a solid foundation in Generative Deep Learning with Python.

Why Advanced Feature Engineering is Essential

In Machine Learning, the quality of input data determines the quality of output predictions. Advanced feature engineering is the key to uncovering hidden patterns and meaningful insights in your data, transforming it into structured inputs that drive model performance. This book provides a deep dive into creating and refining features tailored to your data’s unique challenges, ensuring models are both accurate and insightful.

What You’ll Discover Inside

Feature Engineering for Modern Machine Learning with Scikit-Learn covers every stage of advanced feature engineering, from foundational transformations to automated pipelines and cutting-edge tools:

Automating Data Preparation with Scikit-Learn Pipelines: Learn to create reproducible, automated workflows that handle everything from scaling and encoding to feature selection.
Advanced Feature Creation and Transformation: Master complex techniques like polynomial features, interaction terms, and dimensionality reduction, all designed to improve model accuracy.
Industry-Specific Case Studies: Apply feature engineering techniques to real-world domains like healthcare, retail, and customer segmentation, gaining insights into how feature engineering adapts across fields.
Modern Tools and Automation with AutoML: Explore AutoML tools like TPOT and Auto-sklearn to automate feature selection and model optimization, allowing you to focus on the highest-impact features.
Deep Learning Feature Engineering: Discover techniques tailored for neural networks, including data augmentation, embeddings, and feature transformations that enhance deep learning workflows.

Who Should Read This Book:

Whether you’re an experienced data scientist or an advanced beginner looking to build cutting-edge skills, this book provides essential techniques for modern Machine Learning. It’s ideal for anyone who wants to:

Maximize model performance through impactful feature engineering.
Build efficient, reproducible workflows with Scikit-Learn.
Explore advanced applications across multiple domains.

Elevate Your Models with Advanced Feature Engineering

Feature Engineering for Modern Machine Learning with Scikit-Learn is more than just a guide—it’s a toolkit for creating the data transformations that drive high-performing models. Equip yourself with the latest techniques, tools, and insights to confidently tackle real-world Data Science challenges and unlock the full potential of your Machine Learning projects. Dive into the world of feature engineering and elevate your Data Science expertise today!