| 000 | 02309nam a22002417a 4500 | ||
|---|---|---|---|
| 005 | 20240404104309.0 | ||
| 008 | 240404b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9789353944902 | ||
| 028 | _a: Donated by Pearson | ||
| 082 | _a005.133 FEN | ||
| 100 | _aFenner, Mark E. | ||
| 245 |
_aMachine Learning with Python for Everyone. _c/ Mark E Fenner. |
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| 260 |
_a, Noida _b: Pearson India _c,2023. |
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| 300 |
_axxvi,473p. _c: 26cm. |
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| 365 | _b00 | ||
| 505 | _aChapter 1: Let’s discuss learning Chapter 2: predicting categories: getting started with classification Chapter 3: predicting numerical values: getting started with regression Chapter 4: evaluating and comparing learners Chapter 5: evaluating classifiers Chapter 6: evaluating Regressors Chapter 7: more classification methods Chapter 8: more regression methods Chapter 9: manual feature engineering: manipulating data for fun and Profit Chapter 10: models that engineer features for us Chapter 11: feature engineering for domains: domain-specific learning online chapters Chapter 12: tuning hyperparameters and pipelines Chapter 13: combining learners Chapter 14: connecting, extensions, and further directions | ||
| 520 | _aStudents are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently. Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on Mathematics only where it’s necessary to make connection and deepen insight. | ||
| 650 | _aMachine learning | ||
| 650 | _aPython | ||
| 650 | _aAddison Wesley Data & Analytics Series | ||
| 700 | _aMark E Fenner. | ||
| 942 | _cBOOK | ||
| 999 |
_c61492 _d61492 |
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