000 02235nam a22002537a 4500
999 _c2077
_d2077
003 OSt
005 20190102101605.0
008 190102b ||||| |||| 00| 0 eng d
020 _a978-93-5213-457-1
028 _bAllied Informatics, Jaipur
_c5712
_d17/12/2018
_q2018-19
040 _aBSDU
_bEnglish
_cBSDU
082 _a005.133
_bMUL
100 _aMuller, Andreas C.
245 _aIntroduction to Machine Learning with Python: A guide for data scientists
260 _aMumbai
_bShroff Publishers & Distributors Pvt. Ltd.
_c2018
300 _a378
500 _aMachine 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. 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.
504 _aContents: Introduction Supervised Learning Unsupervised Learning and Preprocessing Representing Data and Engineering Features Model Evaluation and Improvement Algorithm Chains and Pipelines Working with Text Data Wrapping Up Index
520 _aFundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
650 _aComputer Science
650 _aPython
700 _aGuido, Sarah
942 _2ddc
_cBK