book

Machine Learning for the Web

by Steve Essinger, Andrea Isoni

July 2016

Intermediate to advanced

298 pages

6h 14m

English

Packt Publishing

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What this book covers

Downloading the example codeDownloading the color images of this bookErrataPiracyQuestions
General machine-learning conceptsMachine-learning exampleInstalling and importing a module (library)
Using NumPyArrays creationArray manipulationsArray operationsLinear algebra operationsStatistics and mathematical functionsUnderstanding the pandas moduleExploring dataManipulate dataMatplotlib tutorial
Clustering algorithmsDistribution methodsExpectation maximizationMixture of GaussiansCentroid methodsk-meansDensity methodsMean – shiftHierarchical methodsTraining and comparison of the clustering methods
Principal Component Analysis (PCA)PCA example
Model error estimation
Linear regressionRidge regressionLasso regressionLogistic regressionProbabilistic interpretation of generalized linear modelsk-nearest neighbours (KNN)
Multinomial Naive BayesGaussian Naive Bayes
Kernel trick
Regression problemClassification problem
A Python example
Web structure miningWeb crawlers (or spiders)IndexerRanking – PageRank algorithm
Parsing
Information retrieval modelsTF-IDFLatent Semantic Analysis (LSA)Doc2Vec (word2vec)Word2vec – continuous bag of words and skip-gram architecturesMathematical description of the CBOW modelDoc2Vec extensionMovie review query example
Latent Dirichlet allocationModelExampleOpinion mining (sentiment analysis)
Utility matrix
Memory-based Collaborative FilteringUser-based Collaborative FilteringItem-based Collaborative FilteringSimplest item-based Collaborative Filtering – slope oneModel-based Collaborative FilteringAlternative least square (ALS)Stochastic gradient descent (SGD)Non-negative matrix factorization (NMF)Singular value decomposition (SVD)
Item features average methodRegularized linear regression method
Root mean square error (RMSE) evaluationClassification metrics
HTTP – the basics of the GET and POST methodsInstallation and server creationSettings
ModelsURL and views behind HTML web pagesHTML pagesURL declarations and views
Shell interfaceCommandsRESTful application programming interfaces (APIs)
Application setup
Application usage overview
Scrapy settingsScraperPipelinesCrawler
Commands (sentiment analysis model and delete queries)Sentiment analysis model loaderDeleting an already performed querySentiment reviews analyser – Django views and HTML

Content preview from Machine Learning for the Web

Generalized linear models

The generalized linear model is a group of models that try to find the M parameters that form a linear relationship between the labels y_i and the feature vector x⁽ⁱ⁾ that is as follows:

Here, are the errors of the model. The algorithm for finding the parameters tries to minimize the total error of the model defined by the cost function J: