Who This Book Is For
The book is intended for students, educators, Python learners,
machine learning beginners, Data Analysts, Data Scientists,
Machine Learning Engineers, software developers, Business Analysts,
product managers, managers, decision-makers, project teams, and anyone
who wants to understand machine learning not merely as model training,
but as responsible decision support.
Frequently Asked Questions
Is From Models to Decisions in Machine Learning with Python
a book about machine learning with Python?
Yes. The book explains machine learning with Python and shows how
models can be developed with Scikit-Learn, evaluated critically, and
translated into understandable and responsible decisions.
Is the book suitable for machine learning beginners?
Yes. The book is suitable for machine learning beginners, Python
learners, students, educators, and professionals who want to understand
machine learning within a broader decision-making context.
Does the book provide a practical introduction to Scikit-Learn?
Yes. It explains typical machine learning workflows with Scikit-Learn,
including training data, features, train-test splits, cross-validation,
model training, predictions, and model evaluation.
Which machine learning model types are covered?
The book covers classification, regression, clustering, anomaly
detection, recommendation systems, decision trees, random forests,
gradient boosting, and neural networks.
Does the book explain metrics such as accuracy, precision, recall,
and ROC AUC?
Yes. It explains accuracy, precision, recall, F1 score, ROC AUC,
confusion matrices, and regression metrics such as MAE, MSE, RMSE,
and R squared.
Why does the book focus on decisions rather than models alone?
Machine learning models produce predictions, scores, or probabilities
rather than complete decisions. Responsible decisions also require
context, objectives, uncertainty, risks, fairness, explainability,
trade-offs, and human judgment.
Who is this machine learning book for?
The book is intended for students, educators, Python learners,
machine learning beginners, Data Analysts, Data Scientists,
Machine Learning Engineers, software developers, Business Analysts,
product managers, managers, and decision-makers.
Does the book help readers evaluate machine learning models?
Yes. Model evaluation is a central topic, including metrics, baselines,
train-test splits, cross-validation, overfitting, underfitting, feature
importance, uncertainty, risks, and trade-offs.
Does the book cover responsible AI and fairness?
Yes. It discusses responsibility, fairness, explainability, risks,
competing objectives, monitoring, and the responsible integration of
machine learning into real-world decisions.
Where can the book be purchased?
The eBook is available from Amazon Kindle, Apple Books, and Thalia.
Workshops and Lectures Based on the Book
The content can be adapted as a lecture, workshop, or moderated
practical format for universities, companies, educational institutions,
machine learning teams, Data Science teams, managers, project teams,
and organizations.
The focus is on practical questions: What can a machine learning model
actually provide? How should training data, target variables, metrics,
uncertainty, and risks be evaluated? How can predictions be translated
into transparent and responsible decisions?
Understanding Problems and Data
Translate real-world challenges into clear questions, target
variables, data requirements, and decision contexts.
Evaluating Models and Metrics
Examine baselines, model alternatives, evaluation metrics,
overfitting, uncertainty, explainability, and practical limitations.
Developing Responsible Decisions
Make risks, trade-offs, fairness, monitoring requirements, and human
responsibility explicit within machine learning projects.
