This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions i
Probability for Statistics and Machine Learning
โ Scribed by Anirban DasGupta
- Publisher
- Springer
- Year
- 2011
- Tongue
- English
- Leaves
- 795
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
This accessible book provides a versatile treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It contains many worked out examples and exercises.
๐ SIMILAR VOLUMES
<p><span>Using 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
<p>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 ar
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
Springer, 2016. โ 276 p. โ ISBN: 3319307150<div class="bb-sep"></div>Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods<br/>Connects to key open-source Python communities and corresponding modules focused on the latest developments
<p><span>This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:</span></p><p><span>1. </span><span>The basics of probability and statistics: </span><span>These chapters focus on the basics of probability and statistics