| 000 | 02267nam a22002177a 4500 | ||
|---|---|---|---|
| 999 |
_c1331 _d1331 |
||
| 005 | 20211208152903.0 | ||
| 008 | 211208b ||||| |||| 00| 0 eng d | ||
| 020 | _a9789352134571 | ||
| 082 |
_a005.133 _bMUL |
||
| 100 | _aMüller, Andreas C. | ||
| 245 |
_aIntroduction to machine learning with Python : _ba guide for data scientists _cAndreas C Müller; Sarah Guido |
||
| 250 | _aFirst edition | ||
| 260 |
_aSebastopol, CA : _bO'Reilly Media, Inc, _c©2017 |
||
| 300 |
_axii, 378 pages : _billustrations, _c24 cm. |
||
| 505 |
_tIntroduction --
_tSupervised learning -- _tUnsupervised learning and preprocessing -- _tRepresenting data and engineering features -- _tModel evaluation and improvement -- _tAlgorithm chains and pipelines -- _tWorking with text data -- _tWrapping up. |
||
| 520 | _aMachine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you'll learn: Fundamental concepts and applications of machine learning ; Advantages and shortcomings of widely used machine learning algorithms ; How to represent data processed by machine learning, including which data aspects to focus on ; Advanced methods for model evaluation and parameter tuning ; The concept of pipelines for chaining models and encapsulating your workflow ; Methods for working with text data, including text-specific processing techniques ; Suggestions for improving your machine learning and data science skills | ||
| 650 | _aData mining. | ||
| 650 | _aPython (Computer program language) | ||
| 650 | _aMachine learning. | ||
| 942 |
_2ddc _cBK |
||