000			03279cam a2200289zu 4500
001			88869945
003			FRCYB88869945
005			20250107231900.0
006			m o d
007			cr un
008			250108s2019 fr | o|||||0|0|||eng d
020			_a9781789954920
035			_aFRCYB88869945
040			_aFR-PaCSA _ben _c _erda
100	1		_aJohnston, Benjamin
245	0	1	_aApplied Supervised Learning with Python _c['Johnston, Benjamin', 'Mathur, Ishita']
264		1	_bPackt Publishing _c2019
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aJohnston, Benjamin
700	0		_aMathur, Ishita
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88869945 _qtext/html _a
520			_aExplore the exciting world of machine learning with the fastest growing technology in the world Key Features Understand various machine learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into how you can use machine learning in everyday life Book Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learn Understand the concept of supervised learning and its applications Implement common supervised learning algorithms using machine learning Python libraries Validate models using the k-fold technique Build your models with decision trees to get results effortlessly Use ensemble modeling techniques to improve the performance of your model Apply a variety of metrics to compare machine learning models Who this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
999			_c71856 _d71856