000			03057cam a2200289zu 4500
001			88863263
003			FRCYB88863263
005			20250107230842.0
006			m o d
007			cr un
008			250108s2018 fr | o|||||0|0|||eng d
020			_a9781789537864
035			_aFRCYB88863263
040			_aFR-PaCSA _ben _c _erda
100	1		_aBoschetti, Alberto
245	0	1	_aPython Data Science Essentials _c['Boschetti, Alberto', 'Massaron, Luca']
264		1	_bPackt Publishing _c2018
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aBoschetti, Alberto
700	0		_aMassaron, Luca
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88863263 _qtext/html _a
520			_aGain useful insights from your data using popular data science tools Key Features A one-stop guide to Python libraries such as pandas and NumPy Comprehensive coverage of data science operations such as data cleaning and data manipulation Choose scalable learning algorithms for your data science tasks Book Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You'll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learn Set up your data science toolbox on Windows, Mac, and Linux Use the core machine learning methods offered by the scikit-learn library Manipulate, fix, and explore data to solve data science problems Learn advanced explorative and manipulative techniques to solve data operations Optimize your machine learning models for optimized performance Explore and cluster graphs, taking advantage of interconnections and links in your data Who this book is for If you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
999			_c70937 _d70937