The development method of machine learning practice test-driven--Interactive publishing network

This article is a computer Quality Pre-sale recommendation >>>>"machine learning Practice test-driven development method"

A reliable and stable machine learning algorithm is developed using test-driven method.

Editor's recommendation

This book describes how to use a test-driven approach when developing machine learning algorithms to capture errors that can disrupt normal analysis. This practical guide, from the fundamentals of test-driven development and machine learning, demonstrates how to apply test-driven development to a number of machine learning algorithms, including naive Bayesian classifiers and neural networks.
Any machine learning algorithm has some traditional testing methods, but they usually do not take into account the human error in coding. With a test-driven development approach, you will not blindly rely on the results of machine learning as other researchers do, but can reduce the risk of errors and write neat, stable machine learning code. If you're familiar with Ruby 2.1, you're ready to read the book.
by reading this book, you will be able to:
use test-driven methods to write and run tests before you write code
learn the best usage of eight machine learning algorithms and weigh them
test each algorithm by hands-on real-world examples
Understanding the similarity between test-driven development and the scientific approach to validating solutions
Learn about the risks of machine learning, such as less-fitting or overfitting data
explore various technologies that can improve machine learning models or data extraction

Content Introduction

This book mainly describes how to apply test-driven development to machine learning algorithms. Each chapter provides examples of specific problems that machine learning technology can solve, as well as solutions to problems and data processing. It covers test-driven machine learning, machine learning overview, K-Nearest neighbor classification, naive Bayesian classification, Hidden Markov model, support vector machine, neural network, clustering, nuclear ridge regression, model improvement and data extraction. By studying this book, you will be able to use machine learning techniques to solve real-world problems involving data.

As a translator

Matthew Kirk
Modulus is the founder of the 7 company, which provides consulting services for data science and ruby development. Matthew has been in the process of programming for more than 15 years and has lectured on machine learning and data science topics at many technical conferences around the world.

Media Review

"This book is very interesting. Rare for developers who want to learn more about machine learning! " 
--brad ediger,advanced The author of the Rails book
"This book is really great!" " 
co-founder of--starr Horne,honeybadger website
"After reading Matthew Kirk's" Machine learning practice ", I was very fruitful. " 
--james Edward Gray Ii,graysoft, a consultant for the company.

Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.

The development method of machine learning practice test-driven--Interactive publishing network