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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (1 video file, approximately 4 hr., 24 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Programming Foundations of Classification and Regression LiveLessons (Machine Learning with Python for Everyone Series), Part 1 Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing.
    Note: Online resource; Title from title screen (viewed February 11, 2020) , Mode of access: World Wide Web.
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  • 2
    Online Resource
    Online Resource
    [Place of publication not identified] : Addison-Wesley Professional
    ISBN: 9780137932962 , 0137932960
    Language: English
    Pages: 1 online resource (1 video file (5 hr., 55 min.)) , sound, color.
    Edition: Second edition.
    Abstract: Machine Learning with Python for Everyone, Part 1: Learning Foundations, 2nd Edition.
    Note: Vendor-supplied metadata
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  • 3
    Online Resource
    Online Resource
    [Place of publication not identified] : Addison-Wesley Professional
    ISBN: 9780138092818 , 0138092818
    Language: English
    Pages: 1 online resource (1 video file (16 hr., 6 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Live lessons
    DDC: 006.3/1
    Keywords: Machine learning ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Overview 12+ of Video Instruction Machine Learning in Python for Everyone video collection is based on three video courses that teach everything about the foundations and tools for machine learning. As machine learning has moved from futuristic AI projects to data analysis on your desk, you need to begin to build models and start coding machine learning tasks. This master class includes the following courses: Machine Learning with Python for Everyone Part 1: Learning Foundations, 2nd Edition Machine Learning with Python for Everyone, Part 2: Measuring Models Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox Machine Learning with Python for Everyone Part 1: Learning Foundations is code-along sessions moving you from introductory machine learning concepts to concrete code. These videos skew away from heavy mathematics and focus on using Python, scikit-learn. Our emphasis on stories, graphics and code builds your understanding of machine learning. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques. Machine Learning with Python for Everyone, Part 2: Measuring Models teaches the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You learn techniques for getting the most informative learning performance measures out of your data. You come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data. Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox teaches about fundamental classification and regression metrics like decision tree classifiers and regressors, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You learn techniques for feature engineering, including scaling, discretization, and interactions. Finally, you tackle implementing pipelines for more complex processing and nested cross-validation for tuning hyperparameters. About the Instructor Dr. Mark Fenner , owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function. About Pearson Video Training Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
    Note: Online resource; title from title details screen (O'Reilly, viewed February 20, 2023)
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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (1 video file, approximately 4 hr., 37 min.)
    Edition: 1st edition
    Keywords: Electronic videos
    Abstract: Developing Classification and Regression Systems (Machine Learning with Python for Everyone Series), Part 3 Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.
    Note: Online resource; Title from title screen (viewed January 5, 2022) , Mode of access: World Wide Web.
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  • 5
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (353 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
    Note: Online resource; Title from title page (viewed August 12, 2019)
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  • 6
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 45 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. 4 Hours of Video Instruction vDescription Code-along sessions move you from introductory machine learning concepts to concrete code. Overview Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. You learn about the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You will learn techniques for getting the most informative learning performance measures out of your data. You will come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data. About the Instructor Mark Fenner, PhD, has been teaching computing and mathematics to diverse adult audiences since 1999. His research projects have addressed design, implementation, and performance of machine learning and numerical algorithms, learning systems for security analysis of software repositories and intrusion detection, probabilistic models of protein function, and analysis and visualization of ecological and microscopy data. Mark continues to work across the data science spectrum from C, Fortran, and Python implementation to statistical analysis and visualization. He has delivered training and developed curriculum for Fortune 50 companies, boutique consultancies, and national-level research laboratories. Mark holds a Ph.D. in Computer Science and owns Fenner Training and Consulting, LLC. Skill Level Beginner to Intermediate Learn How To Recognize underfitting and overfitting with graphical plots. Make use of resampling techniques like cross-validation to get the most out of your data. Graphically evaluate the learning performance of learning systems Compare production learners with baseline models over various classification metrics Build and evaluate confusion matrices and ROC curves Apply classification metrics to multi-class learning problems Develop precision-recall and lift curves for cla...
    Note: Online resource; Title from title screen (viewed April 23, 2021) , Mode of access: World Wide Web.
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