| 000 | 01000nam a2200193 4500 | ||
|---|---|---|---|
| 001 | 20640679 | ||
| 010 | _a 2018302039 | ||
| 020 | _a9789352137114 | ||
| 082 | 0 | 4 |
_a006.31 _bALI/F |
| 100 | 1 | _aAlice, Zheng | |
| 245 | 1 | 0 |
_aFeature engineering for machine learning : _bprinciples and techniques for data scientists |
| 260 |
_aMumbai _bShroff publishers _c2018 |
||
| 300 |
_axiii, 200 pages : _billustrations ; |
||
| 520 | _aFeature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.-- | ||
| 650 | 7 | _aData mining. | |
| 650 | 7 | _aMachine learning. | |
| 700 | 1 | _aCasari, Amanda, | |
| 942 | _cBK | ||
| 999 |
_c76240 _d76240 |
||