Python for Probability, Statistics, and Machine Learning (Springer)
Monday, 22 July 2019

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.  Author Dr. José Unpingco develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes. Detailed proofs for certain important results are also provided.

<ASIN:3030185443>

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.

Author: Dr. José Unpingco
Publisher: Springer
Date: June 2019
Pages: 384
ISBN: 978-3030185442
Print: 3030185443
Kindle: B07TQCV5VZ
Audience: Python developers with an undergraduate-level exposure to probability, statistics, or machine learning
Level: Intermediate/Advanced
Category: Artificial Intelligence

 

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