000 02176nam a22001937a 4500
020 _a978-3319307152
082 _a005.133 UNP
100 _aUnpingco, Jose
245 _aPython for probability, statistics, and machine learning
260 _aUSA
_bSpringer
_c2016
300 _axiv, 276 p.
_b24 cm ; Hard
500 _aAlpha/2434/ Rs.7399/-
505 _a Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
520 _aThis 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. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes.
650 _aProbabilities--Data processing
_aPython (Computer program language)
_aStatistics--Data processing
_aMathematical statistics
_aData mining
700 _aUnpingco, Jose
856 _uhttp://www.springer.com/in/book/9783319307152
942 _cBK
999 _c103194
_d103194