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Data Science, End to End — Essentials, Vol II: Machine Learning
From Your First Model to Ensembles & Beyond
This book is 100% completeLast updated on 2026-10-16
From your first predictive model to the ensembles that win competitions — the machine-learning core of the Data Science, End to End series, built from first principles in clean, idiomatic Python.
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About the Book
I taught data science for years and kept wishing for one honest, end to end reference, so I wrote it. This is Volume II of three, and it goes from your first predictive model to the ensembles that run in production: the full supervised and unsupervised toolkit (linear and logistic regression, regularization, decision trees, random forests, gradient boosting, k-means and clustering, PCA), plus the evaluation discipline real work depends on (cross-validation, the bias variance trade-off, ROC and AUC, precision and recall, and principled tuning). Every topic opens with a picture you can feel, then names the technical idea behind it. Each volume stands alone; together they run from raw data to large language models in production.
Author
About the Author
Archit Sharma
Archit Sharma is a data science educator and the author of the "Data Science, End to End — Essentials" series. He teaches data science from first principles — the mathematics shown honestly, every concept implemented in clean, idiomatic Python — and writes for practitioners who want to understand what they build, not just copy it. His work spans the full pipeline: the Python, statistics, and SQL foundations; the machine-learning core from regression to gradient boosting; and modern deep learning, NLP, and large language models, through to shipping them in production.
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