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The Orange Book of Machine Learning Green edition. The essentials of making predictions using supervised regression and classification for tabular data Carl McBride Ellis

Książka w języku angielskim angielski
The Orange Book of Machine Learning Green edition. The essentials of making predictions using supervised regression and classification for tabular data Carl McBride Ellis - okladka książki

The Orange Book of Machine Learning Green edition. The essentials of making predictions using supervised regression and classification for tabular data Carl McBride Ellis - okladka książki

The Orange Book of Machine Learning Green edition. The essentials of making predictions using supervised regression and classification for tabular data Carl McBride Ellis - audiobook MP3

The Orange Book of Machine Learning Green edition. The essentials of making predictions using supervised regression and classification for tabular data Carl McBride Ellis - audiobook CD

Autor:
Carl McBride Ellis
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
238
Dostępny format:
     PDF

Ebook
107,10 zł 119,00 zł (-10%)
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Do przechowalni

Master the essential tools and techniques for supervised machine learning on tabular data with this practical guide to regression and classification. Through clear explanations, code snippets, and hands-on notebooks, you'll learn how to use Python and leading machine learning libraries, including pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL, to build predictive models for real-world datasets.
The book covers the complete workflow, from data exploration and cleaning to model development, evaluation, and optimization. You'll learn how to perform regression analysis for accurate point predictions and estimate uncertainty using conformal prediction intervals. For classification tasks, you'll explore probabilistic predictions and calibration techniques to improve model reliability. You'll also discover practical approaches to feature engineering, feature selection, and hyperparameter optimization to enhance model performance. In addition, the book introduces tabular foundation models and in-context learning techniques, providing insight into the latest advances in machine learning for structured data.
By the end of the book, you'll have the skills and confidence to develop, evaluate, and deploy supervised machine learning models for a wide range of tabular data applications.

O autorze książki

Carl is a freelance data scientist based in Madrid, Spain, specializing in predictive analytics and machine learning. He is also an adjunct university lecturer in machine learning and artificial intelligence. Before transitioning into data science, Carl spent more than 20 years as an academic researcher in the physical sciences, focusing on the computer simulation of liquids using molecular dynamics and Metropolis Monte Carlo methods. He has co-authored more than 40 scientific publications and brings extensive experience in data analysis, modeling, and scientific computing. His work combines academic rigor with practical machine learning applications, helping students and professionals develop effective predictive solutions.

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