LEADER 06442nam 22008295 450 001 9910629298103321 005 20240222143021.0 010 $a9783031046483$b(electronic bk.) 010 $z9783031046476 024 7 $a10.1007/978-3-031-04648-3 035 $a(MiAaPQ)EBC7131944 035 $a(Au-PeEL)EBL7131944 035 $a(CKB)25280520400041 035 $a(DE-He213)978-3-031-04648-3 035 $a(PPN)266353932 035 $a(EXLCZ)9925280520400041 100 $a20221104d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPython for Probability, Statistics, and Machine Learning /$fby José Unpingco 205 $a3rd ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (524 pages) 311 08$aPrint version: Unpingco, José Python for Probability, Statistics, and Machine Learning Cham : Springer International Publishing AG,c2023 9783031046476 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index. 330 $aUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming. ˇ Features a novel combination of modern Python implementations and underlying mathematics to illustrate and visualize the foundational ideas of probability, statistics, and machine learning; ˇ Includes meticulously worked-out numerical examples, all reproducible using the Python code provided in the text, that compute and visualize statistical and machine learning models thus enabling the reader to not only implement these models but understand their inherent trade-offs; ˇ Utilizes modern Python modules such as Statsmodels, Tensorflow, Keras, Sympy, and Scikit-learn, along with embedded "Programming Tips" to encourage readers to develop quality Python codes that implement and illustrate practical concepts. 606 $aTelecommunication 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aEngineering mathematics 606 $aEngineering$xData processing 606 $aStatistics 606 $aData mining 606 $aCommunications Engineering, Networks 606 $aProbability and Statistics in Computer Science 606 $aMathematical and Computational Engineering Applications 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aData Mining and Knowledge Discovery 606 $aPython (Llenguatge de programaciķ)$2thub 606 $aAprenentatge automātic$2thub 606 $aProbabilitats$2thub 606 $aProcessament de dades$2thub 608 $aLlibres electrōnics$2thub 615 0$aTelecommunication. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aEngineering mathematics. 615 0$aEngineering$xData processing. 615 0$aStatistics. 615 0$aData mining. 615 14$aCommunications Engineering, Networks. 615 24$aProbability and Statistics in Computer Science. 615 24$aMathematical and Computational Engineering Applications. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aData Mining and Knowledge Discovery. 615 7$aPython (Llenguatge de programaciķ) 615 7$aAprenentatge automātic 615 7$aProbabilitats 615 7$aProcessament de dades 676 $a006.31 676 $a005.133 700 $aUnpingco$b Jose?$f1969-$01075978 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910629298103321 996 $aPython for probability, statistics, and machine learning$93057893 997 $aUNINA