SKU/Artículo: AMZ-B0FSDJRD56
SQL Feature Engineering for Queries: Preparing Data for Machine Learning Models with Advanced Techniques
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Paperback
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0.39 kg
0.39 kg
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Amazon
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USA
Sobre este producto
- Supercharge Your ML Models: Harness SQL for Expert-Level Feature Engineering and Data Preparation!In the high-stakes arena of machine learning, raw data is just the starting point—true success hinges on crafting powerful features that uncover hidden patterns and drive accurate predictions. Yet, many data scientists overlook SQL's immense potential for efficient, scalable feature engineering, often wrestling with cumbersome data exports and fragmented pipelines. If you're ready to streamline your workflows, minimize latency, and build production-ready models directly from your database, "SQL Feature Engineering for Queries: Preparing Data for Machine Learning Models with Advanced Techniques" by Mary John is your game-changing resource.Drawing on real-world expertise in data engineering and ML, this comprehensive guide empowers you to master SQL as a powerhouse for transforming messy datasets into model-optimized features. From handling categorical encodings and numerical scaling to extracting temporal insights and text-based sentiments, you'll learn to perform sophisticated manipulations in-database, leveraging tools like PostgreSQL for seamless integration with Python ecosystems. Forget theoretical overviews—dive into actionable techniques with detailed SQL queries, troubleshooting strategies, and ethical considerations to avoid biases, all while ensuring reproducibility and collaboration through version-controlled scripts.Logically organized across 12 chapters for step-by-step progression:Foundations: Introduction to feature engineering, SQL basics for data prep, and environment setup.Core Techniques: Categorical, numerical, date/time, and text feature engineering; aggregations with window functions; handling imbalanced/missing data.Advanced Integration: Modular pipelines with CTEs and recursion; JSON/array handling; automating with dbt/Airflow; monitoring drift.Real-World Mastery: Case studies in e-commerce, finance, healthcare, and IoT; optimization for large-scale queries; future trends like in-database ML.Appendices: SQL dialect differences (PostgreSQL, MySQL, SQL Server) and sample datasets/code for hands-on practice.What sets this book apart? Its emphasis on practical, database-centric workflows ensures you build features that scale effortlessly, with exercises, case studies (e.g., Titanic churn prediction, housing regression), and a companion GitHub repo for datasets and scripts. Ideal for intermediate data professionals with basic SQL and ML familiarity (e.g., Pandas, Scikit-learn), this first-edition essential assumes access to a relational DBMS and equips you for roles in data science, analytics, or engineering—whether solo or in teams.
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