Python for Probability, Statistics, and Machine Learning [E-Book] / by José Unpingco.
Unpingco, José, (author)
2nd edition 2019.
Cham : Springer, 2019
XIV, 384 pages 165 illustrations, 37 illustrations in color (online resource)
englisch
9783030185459
10.1007/978-3-030-18545-9
Full Text
Table of Contents:
  • 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.