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Python for Probability, Statistics, and Machine Learning

BuchKartoniert, Paperback
509 Seiten
Englisch
Springererschienen am06.11.20233. Aufl.
Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR64,19
BuchGebunden
EUR96,29
BuchKartoniert, Paperback
EUR69,54
E-BookPDF1 - PDF WatermarkE-Book
EUR117,69
E-BookPDF1 - PDF WatermarkE-Book
EUR64,19
E-BookPDF1 - PDF WatermarkE-Book
EUR69,54

Produkt

KlappentextUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.
Details
ISBN/GTIN978-3-031-04650-6
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2023
Erscheinungsdatum06.11.2023
Auflage3. Aufl.
Seiten509 Seiten
SpracheEnglisch
IllustrationenXVII, 509 p. 189 illus., 78 illus. in color.
Artikel-Nr.55093834

Inhalt/Kritik

Inhaltsverzeichnis
Introduction.- Part 1 Getting Started with Scientific Python.- Installation and Setup.- Numpy.- Matplotlib.- Ipython.- Jupyter Notebook.- Scipy.- Pandas.- Sympy.- Interfacing with Compiled Libraries.- Integrated Development Environments.- Quick Guide to Performance and Parallel Programming.- Other Resources.- Part 2 Probability.- Introduction.- Projection Methods.- Conditional Expectation as Projection.- Conditional Expectation and Mean Squared Error.- Worked Examples of Conditional Expectation and Mean Square Error Optimization.- Useful Distributions.- Information Entropy.- Moment Generating Functions.- Monte Carlo Sampling Methods.- Useful Inequalities.- Part 3 Statistics.- Python Modules for Statistics.- Types of Convergence.- Estimation Using Maximum Likelihood.- Hypothesis Testing and P-Values.- Confidence Intervals.- Linear Regression.- Maximum A-Posteriori.- Robust Statistics.- Bootstrapping.- Gauss Markov.- Nonparametric Methods.- Survival Analysis.- Part 4 Machine Learning.- Introduction.- Python Machine Learning Modules.- Theory of Learning.- Decision Trees.- Boosting Trees.- Logistic Regression.- Generalized Linear Models.- Regularization.- Support Vector Machines.- Dimensionality Reduction.- Clustering.- Ensemble Methods.- Deep Learning.- Notation.- References.- Index.mehr

Schlagworte

Autor

Dr. José Unpingco completed his PhD from the University of California (UCSD), San Diego and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data science topics, with deep experience in machine learning. He was the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD) where he also spearheaded the DoD-wide adoption of scientific Python. In his time as the primary scientific Python instructor for the DoD, he taught over 600 scientists and engineers. Dr. Unpingco is currently the Vice President for Machine Learning/Data Science for the Gary and Mary West Health Institute, a non-profit Medical Research Organization in San Diego, California. He is also a lecturer at UCSD for their undergraduate and graduate Machine Learning and Data Science degree programs.