Python for Probability, Statistics, and Machine Learning
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...
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2022, 2022
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| Edition: | 3rd ed. 2022 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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