Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python

Summary: This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.

Record details

ISBN: 9781838826048
ISBN: 1838823581
ISBN: 9781838823580
Physical Description: 1 online resource (1 volume) : illustrations
remote
Publisher: Birmingham, UK : Packt Publishing, Limited, 2020.
Copyright: ©2020

Content descriptions

Formatted Contents Note:

Regularizing the regressor -- Training the lasso regressor -- Finding the optimum regularization parameter -- Finding regression intervals -- Getting to know additional linear regressors -- Using logistic regression for classification -- Understanding the logistic function -- Plugging the logistic function into a linear model -- Objective function -- Regularization -- Solvers -- Configuring the logistic regression classifier -- Classifying the Iris dataset using logistic regression -- Understanding the classifier's decision boundaries -- Getting to know additional linear classifiers -- Summary

Source of Description Note:

Online resource; title from digital title page (viewed on November 23, 2020).