Machine learning with Python for everyone (Record no. 67144)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02042nam a22001457a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789353944902 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | FEN/M |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Fenner, Mark E. |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning with Python for everyone |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Noida |
| Name of publisher | Pearson |
| Year of publication | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 473p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Students 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. table of Contents: Chapter 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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| -- | Artificial intelligence |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | BK |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | |
| Damaged status | |
| Collection code | Home library | Current library | Shelving location | Date acquired | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|---|---|
| Stack | Kannur University Central Library | Kannur University Central Library | Stack | 05/07/2023 | 006.31 FEN/M | 59894 | BK |