SKU/Artículo: AMZ-B0GGWWNLKK
Mastering Machine Learning: Hyperparameter Tuning and Feature Selection Made Simple: A Beginner’s Guide to Building High-Performance Models with Practical, Hands-On Techniques
Format:
Paperback
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En stock
En stock
Peso con empaque:
0.33 kg
0.33 kg
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Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Unlock the secrets to building high-performance machine learning models with Mastering Machine Learning: Hyperparameter Tuning and Feature Selection Made Simple by Adrian J. Ryder. This beginner-friendly guide demystifies the art of ML optimization, showing you how to transform raw data into accurate, efficient predictions. Starting with the fundamentals of machine learning and why optimization is crucial, the book dives into key concepts like hyperparameters (the "knobs" that control model learning) and features (the essential data inputs that drive predictions). You'll explore practical techniques for hyperparameter tuning— from manual methods and grid search to advanced Bayesian optimization— and feature selection strategies, including filter, wrapper, and embedded methods, to eliminate noise and boost accuracy.Through hands-on examples using Python, Scikit-learn, Pandas, and real-world datasets like Iris, learn to avoid common pitfalls like overfitting while applying these skills to applications such as spam detection, price prediction, and customer analysis. Advanced chapters combine tuning and selection for superior results, evaluate model performance, and guide you through projects and next steps in your ML journey.Whether you're a novice coder or aspiring data scientist, this 181-page book equips you with actionable steps, code snippets, and setup instructions to get started immediately. Say goodbye to mediocre models and hello to optimized, reliable AI solutions that work in practice.Don't wait—elevate your ML skills and stay ahead in this booming field. Grab your copy today and build models that deliver real impact!
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AR$28.853
31% OFF
AR$19.893
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