By Gavin Hackeling
Apply powerful studying algorithms to real-world difficulties utilizing scikit-learn
About This Book
• layout and troubleshoot computing device studying structures for universal projects together with regression, class, and clustering
• Acquaint your self with well known desktop studying algorithms, together with selection bushes, logistic regression, and aid vector machines
• a realistic example-based advisor that will help you achieve services in enforcing and comparing desktop studying platforms utilizing scikit-learn
[ Who This publication Is For
If you're a software program developer who desires to find out how computer studying types paintings and the way to use them successfully, this booklet is for you. Familiarity with laptop studying basics and Python might be useful, yet isn't really crucial.
What you are going to study
• overview primary options together with supervised and unsupervised studies, universal initiatives, and function metrics
• expect the values of continuing variables utilizing linear regression
• Create representations of files and photographs that may be utilized in laptop studying types
• Categorize records and textual content messages utilizing logistic regression and help vector machines
• Classify photos by means of their matters
• notice hidden buildings in facts utilizing clustering and visualize advanced info utilizing decomposition
• assessment the functionality of computing device studying structures in universal initiatives
• Diagnose and redress issues of types as a result of bias and variance
This publication examines computing device studying versions together with logistic regression, selection timber, and help vector machines, and applies them to universal difficulties akin to categorizing files and classifying photographs. It starts off with the basics of laptop studying, introducing you to the supervised-unsupervised spectrum, the makes use of of teaching and attempt facts, and comparing versions. you'll how one can use generalized linear types in regression difficulties, in addition to clear up issues of textual content and specific features.
You should be accustomed to using logistic regression, regularization, and a number of the loss features which are utilized by generalized linear types. The publication also will stroll you thru an instance undertaking that activates you to label the main doubtful education examples. additionally, you will use an unmanaged Hidden Markov version to foretell inventory prices.
By the top of the publication, you may be a professional in scikit-learn and should be good versed in desktop studying
Read or Download Mastering Machine Learning with scikit-learn PDF
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