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SKU/Artículo: AMZ-B0G63QP9ZT

Applied Machine Learning - Concepts, Tools, and Case Studies

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.36 kg

Devolución:
No

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

Applied Machine Learning Concepts, Tools, and Case Studies is a code-first, math-light introduction to machine learning that treats ML as a practical craft rather than a theory exam. Written for undergraduate students with basic Python experience and early-career professionals moving into applied ML, it focuses on building real systems with real data, using the Python ecosystem that practitioners actually rely on in the field.Across five parts and seventeen chapters, the book walks through an end-to-end journey. Part I grounds the practitioner in core ideas, types of machine learning, and the ethics of everyday recommendation systems. Part II builds supervised learning skills using scikit-learn, from linear models and regularization to tree-based methods, model comparison, and a full cost-aware fraud detection case study on transaction data.Part III turns to unsupervised learning, including clustering, dimensionality reduction, and manifold methods, all framed through realistic scenarios such as retail segmentation, music taste clustering, toxic-comment structure, and college data exploration. Part IV moves into modern deep learning, starting with perceptrons and multilayer networks, then guiding the practitioner through PyTorch and Keras case studies on topics such as human activity recognition, hospital readmission, bike sharing demand, spam detection, and fairness analysis in recidivism prediction. A full NVIDIA stock price forecasting pipeline shows a complex ensemble left intentionally in an intermediate, not-yet-deployment-ready state, so that the learner can see how rigorous diagnosis, monitoring, and retraining plans are designed in practice.Throughout, every example is heavily commented, built around reproducible Python pipelines, and accompanied by plain-language explanations of metrics, trade-offs, and ethical implications. Ethics notes are integrated directly into technical chapters, treating issues such as fairness, transparency, and responsible automation as first-class topics rather than afterthoughts. The book closes with capstone project guidance and a forward-looking discussion of transformers, self-supervised learning, and MLOps, giving the practitioner a clear path from first scripts to production-minded machine learning.

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