Python for Probability,Statistics,and Machine Learning
Authors: José Unpingco
ISBN-10: 3030185443
ISBN-13: 9783030185442
Edition 版次: 2nd ed. 2019
Publication Date 出版日期: 2019-06-29
Print Length 页数: 384 pages
Book Description
By finelybook
This textbook,fully updated to feature Python version 3.7,covers the key ideas that link probability,statistics,and machine learning illustrated using Python modules. The entire text,including all the figures and numerical results,is reproducible using the Python codes and their associated Jupyter/IPython notebooks,which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes,thereby connecting theoretical concepts to concrete implementations. The update features full coverage of Web-based scientific visualization with Bokeh Jupyter Hub; Fisher Exact,Cohen’s D and Rank-Sum Tests; Local Regression,Spline,and Additive Methods; and Survival Analysis,Stochastic Gradient Trees,and Neural Networks and Deep Learning. Modern Python modules like Pandas,Sympy,and Scikit-learn 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. This book is suitable for classes in probability,statistics,or machine learning and requires only rudimentary knowledge of Python programming.
