最新机器学习入门教材-Python for Probability,Statistics, and Machine Learning Third Edition

This book will teach you the fundamental concepts that underpin probability and statistics and illustrates how they relate to machine learning via the Python language and its powerful extensions. This is not a good first book in any of these topics because we assume that you already had a decent undergraduate-level introduction to probability and statistics. Furthermore, we also assume that you have a good grasp of the basic mechanics of the Python language itself. Having said that, this book is appropriate if you have this basic background and want to learn how to use the scientific Python toolchain to investigate these topics. On the other hand, if you are comfortable with Python, perhaps through working in another scientific field, then this book will teach you the fundamentals of probability and statistics and how to use these ideas to interpret machine learning methods. Likewise, if you are a practicing engineer using a commercial package (e.g., Matlab, IDL), then you will learn how to effectively use the scientific Python toolchain by reviewing concepts with which you are already familiar.

Python for Probability, Statistics, and Machine Learning

Python for Probability, Statistics, and Machine Learning 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Python for Probability,Statistics,and Machine Learning.pdf Python for Probability,Statistics,and Machine Learning.pdf

适用于Springer的Jupyter Notebooks编写了 注意：与相应的

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.