This book covers the key ideas that link probability, statistics, and
machine learning illustrated using Python modules in these areas. 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. 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 anyone with an undergraduate-level exposure to probability,
statistics, or machine learning and with rudimentary knowledge of
Python programming.
0 comments:
Post a Comment