Introduction to machine learning with python : (Record no. 65266)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01874nam a22001697a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200605b2017 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789352134571 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 MUL-A |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Muller, Andreas C. |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to machine learning with python : |
| Remainder of title | a guide for data scientists / |
| Statement of responsibility, etc. | Andreas C. Muller and Sarah Guido |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 378 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 1200.00. |
| 500 ## - GENERAL NOTE | |
| General note | 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 Müller 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 | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| 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 | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Checked out | Date last seen | Date last checked out | Price effective from | Koha item type |
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| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 05/06/2020 | 1200.00 | 18 | 006.31 MUL-A | 41092 | 12/08/2025 | 20/05/2025 | 20/05/2025 | 05/06/2020 | Books |