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

Overview

This book guides you through integrating the power of machine learning with web development using Python. You'll learn how to create effective, predictive web applications leveraging frameworks such as Django, as well as explore real-world use cases like recommendation systems and web mining.

What this Book will help me do

Gain understanding of key machine learning concepts and terminology.
Learn how to perform web data mining using Python libraries.
Master the Django framework for developing interactive web applications.
Discover the best clustering and classification techniques to use in Python.
Complete a hands-on movie recommendation system using Python and Django.

Author(s)

Steve Essinger, with expertise in Python programming and data science, teams up with None Isoni to provide a clear approach to this intersection of web development and machine learning. Both authors bring practical insights and accessible explanations to ensure that readers with varying backgrounds can follow along.

Who is it for?

This book is ideal for aspiring data scientists and developers aiming to combine Python machine learning with practical web development. Some familiarity with statistics and Python programming is expected, but prior experience with Django is not mandatory. The book primarily serves readers eager to understand and implement predictive capabilities into web applications. Developers seeking a guided, hands-on approach will find this book perfectly suited to their needs.