Thoughtful Machine Learning
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Thoughtful Machine Learning With Python
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Author : Matthew Kirk
language : en
Publisher:
Release Date : 2017
Thoughtful Machine Learning With Python written by Matthew Kirk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms.
Thoughtful Machine Learning With Python
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Author : Matthew Kirk
language : en
Publisher:
Release Date : 2017
Thoughtful Machine Learning With Python written by Matthew Kirk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Thoughtful Machine Learning
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Author : Matthew Kirk
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-09-26
Thoughtful Machine Learning written by Matthew Kirk and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-26 with Computers categories.
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
Thoughtful Machine Learning With Python
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Author : Matthew Kirk
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-01-16
Thoughtful Machine Learning With Python written by Matthew Kirk and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-16 with Computers categories.
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
Thoughtful Machine Learning
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Author : Matthew Kirk
language : en
Publisher: Oreilly & Associates Incorporated
Release Date : 2014-10-12
Thoughtful Machine Learning written by Matthew Kirk and has been published by Oreilly & Associates Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-12 with Computers categories.
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
Thoughtful Machine Learning
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Author : Matthew Kirk
language : en
Publisher:
Release Date : 2014
Thoughtful Machine Learning written by Matthew Kirk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Learn how to apply test-driven development (TDD) to machine-learning algorithms{u2014}and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can{u2019}t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you{u2019}re familiar with Ruby 2.1, you{u2019}re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction.
Machine Learning
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Author : Ryszard Stanisław Michalski
language : en
Publisher: Morgan Kaufmann
Release Date : 1983
Machine Learning written by Ryszard Stanisław Michalski and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Computers categories.
Machine Learning
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Author : Lorenza Saitta
language : en
Publisher: Morgan Kaufmann Publishers
Release Date : 1996
Machine Learning written by Lorenza Saitta and has been published by Morgan Kaufmann Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Machine Learning
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Author : Jude Shavlik
language : en
Publisher:
Release Date : 1998
Machine Learning written by Jude Shavlik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.
Welcome To Management How To Grow From Top Performer To Excellent Leader
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Author : Ryan Hawk
language : en
Publisher: McGraw Hill Professional
Release Date : 2020-01-28
Welcome To Management How To Grow From Top Performer To Excellent Leader written by Ryan Hawk and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-28 with Business & Economics categories.
“The ultimate all-in-one guide to becoming a great leader.”—Daniel Pink From the creator and host of The Learning Leader Show, “the most dynamic leadership podcast out there” (Forbes) that will “help you lead smarter” (Inc.), comes an essential tactical guide for newly promoted managers. Every year, millions of top performers are promoted to management-level jobs—only to discover that the tactics that got them promoted are not the tactics that will make them effective in their new role. In Welcome to Management, Ryan Hawk provides practical, actionable advice and tools designed to ensure that transition is a successful one. He presents a new actionable three-part framework distilled from best practices drawn from in-depth interviews with over 300 of the most forward-thinking leaders around the world, as well as his own professional experience going from exceptional individual producer to new leader. Learn how to: • lead yourself: build skills and earn credibility. Compliance can be commanded, but commitment cannot. People reserve their full capacity for emotional commitment for leaders they find credible, and credibility must be earned. • build your team: develop a healthy and sustainable culture of mutual trust and respect that creates cohesion. This includes effective hiring and firing practices. • lead your team: set a clear strategy and vision for your team, communicate effectively, and ultimately drive the results the organization is counting on your team to deliver. Through case studies, hundreds of interviews, and personal stories, the book will help high performers make the leap from individual contributor to manager with greater ease, grace, courage, and effectiveness. Welcome to management!