Python for Probability, Statistics, and Machine Learning [E-Book] / by José Unpingco.
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions i...
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Full text |
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| Personal Name(s): | Unpingco, José, author |
| Edition: |
2nd edition 2019. |
| Imprint: |
Cham :
Springer,
2019
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| Physical Description: |
XIV, 384 pages 165 illustrations, 37 illustrations in color (online resource) |
| Note: |
englisch |
| ISBN: |
9783030185459 |
| DOI: |
10.1007/978-3-030-18545-9 |
| Subject (LOC): |
- 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.