By Gavin Hackeling
Apply potent studying algorithms to real-world difficulties utilizing scikit-learn
About This Book
• layout and troubleshoot laptop studying platforms for universal projects together with regression, class, and clustering
• Acquaint your self with renowned laptop studying algorithms, together with choice timber, logistic regression, and help vector machines
• a realistic example-based consultant that can assist you achieve services in enforcing and comparing computing device studying structures utilizing scikit-learn
[ Who This booklet Is For
If you're a software program developer who desires to find out how computing device studying types paintings and the way to use them successfully, this publication is for you. Familiarity with computer studying basics and Python can be invaluable, yet isn't crucial.
What you'll examine
• evaluation primary techniques together with supervised and unsupervised reports, universal projects, 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 files and textual content messages utilizing logistic regression and aid vector machines
• Classify photographs through their matters
• become aware of hidden buildings in facts utilizing clustering and visualize complicated information utilizing decomposition
• review the functionality of computing device studying structures in universal projects
• Diagnose and redress issues of versions because of bias and variance
This publication examines laptop studying versions together with logistic regression, selection bushes, and help vector machines, and applies them to universal difficulties reminiscent of categorizing records and classifying pictures. It starts with the basics of laptop studying, introducing you to the supervised-unsupervised spectrum, the makes use of of educating and attempt facts, and comparing types. you'll methods to use generalized linear types in regression difficulties, in addition to resolve issues of textual content and express features.
You could be conversant in using logistic regression, regularization, and many of the loss services which are utilized by generalized linear types. The e-book also will stroll you thru an instance venture that activates you to label the main doubtful education examples. additionally, you will use an unmonitored Hidden Markov version to foretell inventory prices.
By the tip of the publication, you'll be knowledgeable in scikit-learn and may be good versed in laptop studying
Read or Download Mastering Machine Learning with scikit-learn PDF
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Python is instantly turning into the go-to language for analysts and knowledge scientists as a result of its simplicity and suppleness, and in the Python facts house, scikit-learn is the unequivocal selection for computer studying. Its constant API and plethora of beneficial properties aid clear up any computing device studying challenge it comes throughout.
Observe potent studying algorithms to real-world difficulties utilizing scikit-learn
About This booklet
• layout and troubleshoot computer studying structures for universal projects together with regression, type, and clustering
• Acquaint your self with renowned desktop studying algorithms, together with choice bushes, logistic regression, and aid vector machines
• a pragmatic example-based consultant that will help you achieve services in imposing and comparing desktop studying platforms utilizing scikit-learn
[ Who This ebook Is For
If you're a software program developer who desires to find out how desktop studying types paintings and the way to use them successfully, this publication is for you. Familiarity with laptop studying basics and Python may be necessary, yet isn't crucial.
What you are going to research
• overview primary strategies together with supervised and unsupervised stories, universal projects, and function metrics
• expect the values of constant variables utilizing linear regression
• Create representations of records and pictures that may be utilized in desktop studying types
• Categorize files and textual content messages utilizing logistic regression and help vector machines
• Classify photos by way of their matters
• detect hidden constructions in information utilizing clustering and visualize complicated facts utilizing decomposition
• review the functionality of desktop studying structures in universal projects
• Diagnose and redress issues of versions because of bias and variance
This booklet examines desktop studying types together with logistic regression, determination timber, and aid vector machines, and applies them to universal difficulties corresponding to categorizing records and classifying photos. It starts off with the basics of desktop studying, introducing you to the supervised-unsupervised spectrum, the makes use of of teaching and try out info, and comparing types. you are going to methods to use generalized linear types in regression difficulties, in addition to remedy issues of textual content and express features.
You may be familiar with using logistic regression, regularization, and some of the loss services which are utilized by generalized linear types. The ebook 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 knowledgeable in scikit-learn and should be good versed in laptop studying
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Extra info for Mastering Machine Learning with scikit-learn
You've successfully installed scikit-learn. We will use a few of panda's convenient tools for importing data and calculating summary statistics. html. Semisupervised approaches make use of both labeled and unlabeled training data. We also reviewed the bias-variance trade-off and discussed common performance measures for different machine learning tasks. In the next chapter, we will discuss the regression task in more detail, and build our first machine learning model with scikitlearn. We will work through a toy problem to learn how the models and learning algorithms work before discussing an application with a larger dataset.
When the system incorrectly classifies a benign tumor as being malignant, the prediction is a false positive. These four outcomes can be used to calculate several common measures of classification performance, including accuracy, precision, and recall. Recall measures the fraction of truly malignant tumors that were detected. A different classifier with lower accuracy and higher recall might be better suited to the task, since it will detect more of the malignant tumors. In the next chapter, we will discuss some common performance measures for regression tasks.
Unsupervised learning problems do not have an error signal to measure; instead, performance metrics for unsupervised learning problems measure some attributes of the structure discovered in the data. Most performance measures can only be calculated for a specific type of task. While this may seem obvious, the following example describes the use of a performance measure that is appropriate for the task in general but not for its specific application. In this problem, however, failing to identify malignant tumors is likely to be a more severe error than mistakenly classifying benign tumors as being malignant.
Mastering Machine Learning with scikit-learn by Gavin Hackeling