Thoughtful Machine Learning : A Test-Driven Approach DOC download ebook
9781449374068 English 1449374069 Apply a fully test-driven approach to machine-learning algorithms, and save yourself the pain of missing mistakes in your analyses. Most data scientists have run an analysis and simply accepted any answer that wasn't an error message. But just because it runs doesn't mean it's correct. Missed mistakes can ruin research and harm reputations. All of that can be avoided by writing tests and building checks into your work. This book shows you how to write tests and build checks into their work. Using the Ruby programming language, software developers, business analysts, and CTOs will learn how to test machine-learning code, and understand what's happening "behind the scenes." Code machine-learning algorithms in a test-driven way Gain confidence to utilize machine learning Dissect algorithms from the granular pieces using unit tests Get real-world examples of utilizing machine learning code, 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
9781449374068 English 1449374069 Apply a fully test-driven approach to machine-learning algorithms, and save yourself the pain of missing mistakes in your analyses. Most data scientists have run an analysis and simply accepted any answer that wasn't an error message. But just because it runs doesn't mean it's correct. Missed mistakes can ruin research and harm reputations. All of that can be avoided by writing tests and building checks into your work. This book shows you how to write tests and build checks into their work. Using the Ruby programming language, software developers, business analysts, and CTOs will learn how to test machine-learning code, and understand what's happening "behind the scenes." Code machine-learning algorithms in a test-driven way Gain confidence to utilize machine learning Dissect algorithms from the granular pieces using unit tests Get real-world examples of utilizing machine learning code, 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