Isbn: 9781491924136 - thoughtful machine learning with python: a test-driven approach (10 Ergebnisse)

Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: New. In English.

Zustand: New. Featuring graphs and highlighted code throughout, Thoughtful Machine Learning with Python guides you through the process of writing problem-solving code, and in the process teaches you how to approach problems through scientific deduction and clever algorithms. Num Pages: 250 pages. BIC Classification: UMW; UYQM. Category: (P) Professional & Vocational. Dimension: 233 x 178 x 15. Weight in Grams: 666. . 2017. 1st Edition. Paperback. . . . . Books ship from the US and Ireland. …

Paperback. Zustand: Brand New. 250 pages. 9.00x6.75x0.25 inches. In Stock.

Zustand: New. 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.&Uumlber den Autor.

Taschenbuch. Zustand: Neu. Neuware - 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.…