Bøger af Matthew Kirk

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 Pythons Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If youre 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 exercisesApply test-driven development (TDD) to write and run tests before you start codingExplore techniques for improving your machine-learning models with data extraction and feature developmentWatch out for the risks of machine learning, such as underfitting or overfitting dataWork with K-Nearest Neighbors, neural networks, clustering, and other algorithms

Learn how to apply test-driven development (TDD) to machine-learning algorithmsand 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 cant 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 youre familiar with Ruby 2.1, youre ready to start.Apply TDD to write and run tests before you start codingLearn the best uses and tradeoffs of eight machine learning algorithmsUse real-world examples to test each algorithm through engaging, hands-on exercisesUnderstand the similarities between TDD and the scientific method for validating solutionsBe aware of the risks of machine learning, such as underfitting and overfitting dataExplore techniques for improving your machine-learning models or data extraction