Introduction to machine learning with Python: a guide for data scientists (Record no. 555)
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| fixed length control field | 01928nam a22001937a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200110172537.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200110b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789352134571 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | MUL |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Muller, Andreas C. |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to machine learning with Python: a guide for data scientists |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | O'Reilly Media |
| Place of publication, distribution, etc. | Sebastopol |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 378 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 1200.00 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.<br/><br/>You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas MÂller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.<br/><br/>With this book, you'll learn:<br/><br/>Fundamental concepts and applications of machine learning<br/>Advantages and shortcomings of widely used machine learning algorithms<br/>How to represent data processed by machine learning, including which data aspects to focus on<br/>Advanced methods for model evaluation and parameter tuning<br/>The concept of pipelines for chaining models and encapsulating your workflow<br/>Methods for working with text data, including text-specific processing techniques<br/>Suggestions for improving your machine learning and data science skills |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Programming languages (Electronic computers) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Total Renewals | Full call number | Accession Number | Date last seen | Date checked out | Copy number | Cost, replacement price | Price effective from | Koha item type | Bill No | Bill Date |
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| Dewey Decimal Classification | IT & Decisions Sciences | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 01/10/2020 | Overseas Press India Private | 898.80 | 9 | 6 | 006.31 MUL | 000829 | 03/14/2024 | 12/16/2023 | 1 | 1200.00 | 01/10/2020 | Book | IN28966 | 31-12-2019 |