Python for Probability Statistics and Machine Learning

Python for Probability Statistics and Machine Learning 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 in machine learning by working with meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations.

Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.

Python for Probability Statistics and Machine Learning


1 Getting Started with Scientific Python1
2 Probability39
3 Statistics123
4 Machine Learning237

About the author 

Dr José Unpingco completed his PhD at the University of California, San Diego in 1997 and has since worked in industry as an engineer, consultant, and instructor on a wide variety of advanced data processing and analysis topics, with deep experience in machine learning and statistics. As the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD), he spearheaded the DoD-wide adoption of scientific Python. He also trained over 600 scientists and engineers to effectively utilize Python for a wide range of scientific topics — from weather modelling to antenna analysis. Dr Unpingco is the co-founder and Senior Director for Data Science at a non-profit Medical Research Organization in San Diego, California. He also teaches programming for data analysis at the University of California, San Diego for engineering undergraduate/graduate students. He is the author of Python for Signal Processing (Springer 2014) and Python for Probability, Statistics, and Machine Learning (2016)

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