Thoughtful Machine Learning With Python
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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 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:
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.
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
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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
Data Science For Beginners
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Author : Leonard Deep
language : en
Publisher:
Release Date : 2019-07-03
Data Science For Beginners written by Leonard Deep and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-03 with categories.
★★ Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★ Have you ever wondered how speech recognition and search engines really work? Do you wish you could get a machine to do more of your tasks? Even if you are brand new to programming, you can learn how to use Python and Machine Learning to make your life easier or develop a satisfying career in a growth industry. You probably use Machine Learning countless times daily. Your search engine or a chess app, the GPS that gives you turn-by-turn driving directions, an app that predicts the next word you want to type or translates your voice to text: they all use Machine Learning. If you are interested in programming and want to understand Python and Machine Learning, the thoughtful, systematic approach to learning in this two-volume bundle will help you get started in this growing field even if you are a novice. Machine Learning for Beginnerscovers the basic knowledge you need and explores all of the cool accomplishments this kind of programming language allows. It answers these and other questions: What is data science and why is it important? What is machine learning and what the benefits of this kind of programming? What is the difference between machine learning and artificial intelligence? What basics and building blocks do you need to know about machine learning? How do supervised machine learning, unsupervised machine learning, and reinforcement machine learning differ? What tips will help you the most out of machine learning? Python Machine Learning for Beginners, the ultimate guide for newbies, provides easy-to-understand chapters to guide you through the early stages of Python programming, considered an excellent program choice for beginners. Topics include: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Python Machine Learning for Beginnersuses examples and exercises to help you retain the information. Machine Learning for Beginners provides the tools you need to enjoy the many benefits of using machine learning for some of your programming needs. Scroll back up to the top of this page and hit BUY IT NOW to get your copy and start learning how to write your own machine learning programs.
Introduction To Machine Learning With Python
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Author : Andreas C. Müller
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-09-26
Introduction To Machine Learning With Python written by Andreas C. Müller 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 2016-09-26 with Computers categories.
Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
Learn From The Experts
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Author : Jeff Bleiel
language : en
Publisher:
Release Date : 2018
Learn From The Experts written by Jeff Bleiel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"In this interview, Jeff Bleiel talks to Matthew Kirk, founder of Your Chief Scientist and the author of Thoughtful Machine Learning with Python about what AI can do for businesses, and the role of AI in a business strategy."--Resource description page.
Mitigating Bias In Machine Learning
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Author : Carlotta A. Berry
language : en
Publisher: McGraw Hill Professional
Release Date : 2024-10-18
Mitigating Bias In Machine Learning written by Carlotta A. Berry 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 2024-10-18 with Technology & Engineering categories.
This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning