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Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
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.
Features fully updated explanation on how to simulate, conceptualize, and visualize random statistical processes
Includes newest Python code and connects to key open-source communities and corresponding modules
Outlines probability, statistics, and machine learning concepts
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.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Nachrichtentechnik |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xvii
509 S. 111 s/w Illustr. 78 farbige Illustr. 509 p. 189 illus. 78 illus. in color. |
| ISBN-13: | 9783031046476 |
| ISBN-10: | 3031046471 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Unpingco, José |
| Auflage: | 3rd ed. 2022 |
| Hersteller: | Springer International Publishing |
| Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
| Maße: | 241 x 160 x 32 mm |
| Von/Mit: | José Unpingco |
| Erscheinungsdatum: | 05.11.2022 |
| Gewicht: | 1,047 kg |
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.
Features fully updated explanation on how to simulate, conceptualize, and visualize random statistical processes
Includes newest Python code and connects to key open-source communities and corresponding modules
Outlines probability, statistics, and machine learning concepts
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.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Nachrichtentechnik |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xvii
509 S. 111 s/w Illustr. 78 farbige Illustr. 509 p. 189 illus. 78 illus. in color. |
| ISBN-13: | 9783031046476 |
| ISBN-10: | 3031046471 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Unpingco, José |
| Auflage: | 3rd ed. 2022 |
| Hersteller: | Springer International Publishing |
| Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
| Maße: | 241 x 160 x 32 mm |
| Von/Mit: | José Unpingco |
| Erscheinungsdatum: | 05.11.2022 |
| Gewicht: | 1,047 kg |
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