Silas Verne

Python for Advanced Machine Learning: From Feature Engineering to Scalable AI Models (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Are you a data scientist striving to elevate your models beyond accuracy plateaus?

Do you want to master feature engineering across numerical, textual, time-series, and image data?

Are you looking for a complete, scalable, and production-ready approach to building intelligent AI pipelines?

If you answered YES to at least one of these questions, you MUST KEEP READING...

Build better models, faster and smartertransform your AI projects with advanced feature engineering and real-world pipeline design

In the evolving world of artificial intelligence, simply training models isn't enough. Many machine learning practitioners struggle with underperforming models, unreliable predictions, and pipelines that can't scale. The problem? They underestimate the power of engineered features. Without strong foundations in preprocessing, data transformation, and domain-specific feature engineering, even the most sophisticated algorithms fail to deliver. But you're not alone in this journey...

This book is your practical guide to mastering advanced machine learning through a unique, hands-on approach

Crafted for experienced practitioners and aspiring AI experts, this book delivers a comprehensive methodology for building superior machine learning systems. You'll uncover cutting-edge techniques in feature engineering, deep learning-based extractions, and real-world deployment strategies. Whether you work with text, images, time-series, or tabular data, this resource shows you how to create features that drive real impact. Backed by years of industry experience and research, this book introduces a unique method that brings together automation, fairness, and scalabilityall in one.

What you'll discover inside:

• Data Preprocessing Mastery: How to handle missing values, detect outliers, and scale your data effectively
• High-Impact Feature Engineering: Strategies to extract, encode, and transform features for maximum model performance
• Domain-Specific Approaches: Best practices for engineering features from text, images, and time-series data
• Feature Selection Methods: Implement filter, wrapper, and embedded techniques to isolate the most predictive variables
• Production-Ready Pipelines: Build scalable systems, handle feature drift, and automate engineering in real-time ML applications
• And much more.

Imagine deploying machine learning models that are more accurate, more explainable, and ready for productionacross any domain or data type

With the knowledge in this book, you'll confidently handle complex data, create scalable pipelines, and transform your models into reliable, high-performing solutions. Unlock the full potential of Python and feature engineeringyour AI projects will never be the same.

Get your copy now and take your machine learning skills to an expert level!

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, CY, CZ, D, DK, EW, E, FIN, F, GR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Silas Verne
• Erscheinungstermin: 1. Mai 2025
• Englisch
• ISBN-13: 9798227792938
• Artikelnr.: 74048277