03313nam a22004335i 4500 ssj0002239883 WaSeSS 20220420141943.0 m d cr n 191001s2019 xxu| o |||| 0|eng d 9781484249468 10.1007/978-1-4842-4947-5 doi WaSeSS AIMIT LIBRARY Q334-342 UYQ bicssc COM004000 bisacsh UYQ thema 006.31 2 SWAM EBOOK Swamynathan, Manohar. 30557 Mastering Machine Learning with Python in Six Steps : a Practical Implementation Guide to Predictive Data Analytics Using Python / by Manohar Swamynathan. 2nd ed. Berkeley, CA : Apress : Imprint: Apress, 2019. xvii, 457p. 25.3 cm. Chapter 1: Step 1 - Getting Started with Python -- Chapter 2 : Step 2 - Introduction to Machine Learning -- Chapter 3: Step 3 - Fundamentals of Machine Learning -- Chapter 4: Step 4 - Model Diagnosis and Tuning -- Chapter 5: Step 5 - Text Mining, NLP AND Recommender Systems -- Chapter 6: Step 6 - Deep and Reinforcement Learning -- Chapter 7 : Conclusion. Requires an SPL library card. Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version's approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. Mode of access: World Wide Web. Artificial intelligence 30558 Big data 30559 Open source software 30560 Computer programming 30561 Electronic books. local 30562 View this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484249475/?ar An e-book available through full-text database. ddc BK 2nd. 006.31 SWAM 222510 222510 03655696 0 0 ddc 0 0 MCA AIMIT AIMIT 2022-03-24 Biblios Book Point 3038.00 Bill no:6627; Bill dt:2022-03-23 1 006.31 SWAM MCA17126 2026-05-23 00:00:00 2025-11-19 1899.00 2022-04-20 BK 0 0 ddc 0 0 MCA AIMIT AIMIT 2022-03-24 Biblios Book Point 3038.00 Bill no:6627; Bill dt:2022-03-23 0 006.31 SWAM MCA17127 2026-05-23 00:00:00 1899.00 2022-04-20 BK