| 000 -LEADER |
|---|
| fixed length control field |
nam a22 7a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230923110219.0 |
| 020 ## - ISBN |
|---|
| International standerd number |
9789353944902 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
AEF |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23rd ed. |
| Classification number |
005.133 |
| Item number |
FEN |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Fenner, M. E. |
| 9 (RLIN) |
3416 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning with python for everyone |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Chennai: |
| Name of publisher, distributor, etc. |
Pearson, |
| Date of publication, distribution, etc. |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
473 p.: |
| Dimensions |
24 cm |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Students are rushing 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 &ldquocode-alongs," and easy-to-understand images focusing on mathematics only where it's necessary to make connections and deepen insight." |
| Miscellaneous information |
Table of Content Chapter <br/>1: Let's Discuss Learning Chapter <br/>2: Predicting Categories: Getting Started with Classification Chapter <br/>3: Predicting Numerical Values: Getting Started with Regression Chapter <br/>4: Evaluating and Comparing Learners Chapter <br/>5: Evaluating Classifiers Chapter <br/>6: Evaluating Regressors Chapter <br/>7: More Classification Methods Chapter <br/>8: More Regression Methods Chapter <br/>9: Manual Feature Engineering: Manipulating Data for Fun and Profit Chapter <br/>10: Models That Engineer Features for Us Chapter <br/>11: Feature Engineering for Domains: Domain-Specific Learning Online Chapters Chapter <br/>12: Tuning Hyperparameters and Pipelines Chapter <br/>13: Combining Learners Chapter <br/>14: Connections, Extensions, and Further Directions Salient |
| Statement of responsibility |
Features <br/>1. Covers whatever learners need to succeed in data science with Python: process, code, and implementation <br/>2. Enables learners to understand the machine learning process, leverage the powerful Python scikit-learn library, and master the algorithmic components of learning systems <br/>3. Integrates clear narrative, carefully designed Python code, images, and interesting, intelligible datasets |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
3167 |
| Topical term or geographic name entry element |
Machine learning. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Book |
