Machine Learning with PyTorch & Scikit-Learn: Crash Course 2027: A Hands-On Guide to Building, Evaluating, and Deploying Python Models - Softcover

Name: Machine Learning with PyTorch & Scikit-Learn: Crash Course 2027: A Hands-On Guide to Building, Evaluating, and Deploying Python Models
Author: McCLURE, BLAXILL; SCAWEN, IAN
ISBN: 9798164511388

McCLURE, BLAXILL ; SCAWEN, IAN

9798164511388: Machine Learning with PyTorch & Scikit-Learn: Crash Course 2027: A Hands-On Guide to Building, Evaluating, and Deploying Python Models

Softcover

ISBN 13: 9798164511388

Verlag: Independently published, 2026

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Ninety-five percent accuracy.
That is what a model achieves when it predicts "no" for every single case in a dataset where only five percent of rows are positive. It is also a model that identifies nothing, catches no fraud, and diagnoses no patient. That one piece of arithmetic is why this book teaches you to read a confusion matrix before you read an accuracy score, why it asks you to state the threshold that produced a label, and why it refuses to accept a reported metric without the baseline and the cross-validation spread beside it. Machine learning is full of numbers that look impressive and mean very little. Learning to tell them apart is the actual skill.

If that distinction between a figure that looks good and a result you can defend is one you want to build into your own practice, this book was written for you.

Machine Learning with PyTorch & Scikit-Learn: Crash Course 2027 is a project-driven guide that takes you from a clean environment to a deployed, documented model. Every chapter adds one component to a working system, and every subchapter ends with code you have run yourself rather than syntax you have read. You will build preprocessing pipelines that cannot leak, regularized regression and classification models with honest evaluation protocols, density-based and prototype-based clustering, convolutional networks for images, fine-tuned transformers for text, and a served API that reports its own model version.

What you gain:

� The ability to spot leakage, selection optimism, and class imbalance before they reach a report.
� A repeatable workflow: frame the question, set the success criterion, split by deployment setting, evaluate against a baseline, diagnose residuals and confusion matrices, persist the full prediction path, monitor for drift.
� Artifacts that another person can load and reproduce, with recorded versions, thresholds, and feature contracts.
� The judgment to know when a neural network is the wrong tool, and the evidence to justify it when it is right.

This is not a passive read. Each subchapter carries exercises that force you to break something and repair it, common pitfalls drawn from the failures that actually occur, and FAQs that answer the questions readers raise at exactly the point they arise. You will be asked to compute a population stability index by hand, to plot a residual against a fitted value and explain the curve, and to reconstruct your own repository from its documentation alone.

If you want to stop running tutorials and start shipping models that hold up under questioning, open Chapter 1.1 and build the environment. The work starts on the first page.