Python for probability, statistics, and machine learning / José Unpingco.
Unpingco, José, 1969- author.
2022
QA76.73.P98
Available Online

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Title Python for probability, statistics, and machine learning / José Unpingco.
Author Unpingco, José, 1969- author.
Edition Third edition.
ISBN 9783031046483 (electronic bk.)
303104648X (electronic bk.)
9783031046476
3031046471
DOI https://doi.org/10.1007/978-3-031-04648-3
Published Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource (1 volume) : illustrations (black and white, and color).
Other Standard Identifiers 10.1007/978-3-031-04648-3 doi
Call Number QA76.73.P98
Dewey Decimal Classification 005.13/3
Summary 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. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. 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. Features a novel combination of modern Python implementations and underlying mathematics to illustrate and visualize the foundational ideas of probability, statistics, and machine learning; Includes meticulously worked-out numerical examples, all reproducible using the Python code provided in the text, that compute and visualize statistical and machine learning models thus enabling the reader to not only implement these models but understand their inherent trade-offs; Utilizes modern Python modules such as Statsmodels, Tensorflow, Keras, Sympy, and Scikit-learn, along with embedded "Programming Tips" to encourage readers to develop quality Python codes that implement and illustrate practical concepts.
Bibliography, etc. Note Includes bibliographical references.
Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Available in Other Form Python for probability, statistics, and machine learning.
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Record Appears in Online Resources > Ebooks
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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.

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