A01=Matthew Kirk
Age Group_Uncategorized
Age Group_Uncategorized
Author_Matthew Kirk
automatic-update
Category1=Non-Fiction
Category=UYQM
COP=United States
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Language_English
machine learning testing ruby data sets data analysis data science scientists algorithms code developers real world
PA=Available
Price_€20 to €50
PS=Active
softlaunch
Product details
- ISBN 9781449374068
- Weight: 430g
- Dimensions: 170 x 235mm
- Publication Date: 11 Nov 2014
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
Secure
checkout
Fast Shipping
Easy returns
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
Matthew Kirk holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He started Modulus 7, a data science and Ruby development consulting firm, in early 2012. Matthew has spoken around the world about using machine learning and data science with Ruby.
