Feature Engineering For Modern Machine Learning With Scikit Learn
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Feature Engineering for Modern Machine Learning with Scikit-Learn
This Book grants Free Access to our e-learning Platform, which includes: ✅ Free Repository Code with all code blocks used in this book ✅ Access to Free Chapters of all our library of programming published books ✅ Free premium customer support ✅ Much more... 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 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. 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!
Feature Engineering for Modern Machine Learning with Scikit-Learn
Author: Cuantum Technologies LLC
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-01-23
Master feature engineering with Scikit-Learn! Learn to preprocess, transform, and automate data for machine learning. Boost predictive accuracy with pipelines, clustering, and advanced techniques for real-world projects. Key Features Comprehensive guide to feature engineering for Scikit-Learn Hands-on projects for real-world applications Focus on automation, pipelines, and deep learning integration Book DescriptionFeature engineering is essential for building robust predictive models. This book delves into practical techniques for transforming raw data into powerful features using Scikit-Learn. You'll explore automation, deep learning integrations, and advanced topics like feature selection and model evaluation. Learn to handle real-world data challenges, enhance accuracy, and streamline your workflows. Through hands-on projects, readers will gain practical experience with techniques such as clustering, pipelines, and feature selection, applied to domains like retail and healthcare. Step-by-step instructions ensure a comprehensive learning journey, from foundational concepts to advanced automation and hybrid modeling approaches. By combining theory with real-world applications, the book equips data professionals with the tools to unlock the full potential of machine learning models. Whether working with structured datasets or integrating deep learning features, this guide provides actionable insights to tackle any data transformation challenge effectively.What you will learn Create data-driven features for better ML models Apply Scikit-Learn pipelines for automation Use clustering and feature selection effectively Handle imbalanced datasets with advanced techniques Leverage regularization for feature selection Utilize deep learning for feature extraction Who this book is for Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.
Feature Engineering for Machine Learning
Author: Alice Zheng
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-03-23
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques