000 | 03289cam a2200493 i 4500 | ||
---|---|---|---|
001 | 22019319 | ||
003 | OSt | ||
005 | 20241219093646.0 | ||
008 | 210503t20202020nyua 001 0 eng d | ||
010 | _a 2020455630 | ||
015 |
_aGBC154077 _2bnb |
||
016 | 7 |
_a019827535 _2Uk |
|
020 |
_a1617295833 _qpaperback |
||
020 |
_a9781617295836 _qpaperback |
||
035 | _a(OCoLC)on1083681967 | ||
040 |
_aYDX _beng _cAmiu _dJRZ _dBDX _dOCLCF _dNYP _dYDXIT _dUKMGB _dDLC |
||
042 | _alccopycat | ||
050 | 0 | 0 |
_aQ325.5 _b.H83 2020 |
100 | 1 |
_aHudgeon, Doug, _eauthor |
|
245 | 1 | 0 |
_aMachine learning for business : _busing Amazon SageMaker and Jupyter / _cDoug Hudgeon and Richard Nichol. |
246 | 3 | 0 | _aUsing Amazon SageMaker and Jupyter |
264 | 1 |
_aShelter Island, NY : _bManning Publications Co., _c[2020] |
|
264 | 4 | _c©2020 | |
300 |
_axxi, 256 pages : _billustrations ; _c24 cm |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_aunmediated _bn _2rdamedia |
||
338 |
_avolume _bnc _2rdacarrier |
||
500 | _aIncludes index. | ||
505 | 0 | _aPart 1. Machine learning for business. How machine learning applies to your business -- Part 2. Six scenarios: machine learning for business. Should you send a purchase order to a technical approver? -- Should you call a customer because they are at risk of churning? -- Should an incident be escalated to your support team? -- Should you question an invoice sent by a supplier? -- Forecasting your company's monthly power usage -- Improving your company's monthly power usage forecast -- Part 3. Moving machine learning into production. Serving predictions over the web -- Case studies. | |
520 |
_a"Machine learning can deliver hugs benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how. "Machine learning for business" teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you'll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you'll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results."-- _cProvided by publisher |
||
583 |
_aCataloging Notes: _c20241219 _kSTAMIU-0199STAMIU-0199 |
||
650 | 0 | _aMachine learning. | |
650 | 0 |
_aArtificial intelligence _xIndustrial applications. |
|
650 | 0 |
_aBusiness enterprises _xTechnological innovations. |
|
650 | 0 |
_aIndustrial productivity _xAutomation. |
|
650 | 7 |
_aArtificial intelligence _xIndustrial applications. _2fast _0(OCoLC)fst00817262 |
|
650 | 7 |
_aBusiness enterprises _xTechnological innovations. _2fast _0(OCoLC)fst00842646 |
|
650 | 7 |
_aIndustrial productivity _xAutomation. _2fast _0(OCoLC)fst00971504 |
|
650 | 7 |
_aMachine learning. _2fast _0(OCoLC)fst01004795 |
|
700 | 1 |
_aNichol, Richard, _eauthor |
|
906 |
_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg |
||
942 |
_2lcc _cBK _n0 |
||
999 |
_c21101 _d21101 |