Machine learning for the web

Machine learning for the web / Andrea Isoni.

Explore the web and make smarter predictions using PythonAbout This Book Targets two big and prominent markets where sophisticated web apps are of need and importance. Practical examples of building machine learning web application, which are easy to follow and replicate. A comprehensive tutorial on...

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Bibliographic Details
Main Author: Isoni, Andrea (Author)
Format: eBook
Language:English
Published: Birmingham : Packt Publishing, 2016.
Subjects:
Machine learning.
Python (Computer program language)
Machine learning
Online Access:Click for online access
Table of Contents:
  • Preface; Introduction to Practical Machine Learning Using Python; General machine-learning concepts; Machine-learning example; Installing and importing a module (library); Preparing, manipulating and visualizing data
  • NumPy, pandas and matplotlib tutorials; Using NumPy; Arrays creation; Array manipulations; Array operations; Linear algebra operations; Statistics and mathematical functions; Understanding the pandas module; Exploring data; Manipulate data; Matplotlib tutorial; Scientific libraries used in the book; When to use machine learning; Summary; Unsupervised Machine Learning.
  • Clustering algorithmsDistribution methods; Expectation maximization; Mixture of Gaussians; Centroid methods; k-means; Density methods; Mean
  • shift; Hierarchical methods; Training and comparison of the clustering methods; Dimensionality reduction; Principal Component Analysis (PCA); PCA example; Singular value decomposition; Summary; Supervised Machine Learning; Model error estimation; Generalized linear models; Linear regression; Ridge regression; Lasso regression; Logistic regression; Probabilistic interpretation of generalized linear models; k-nearest neighbours (KNN); Naive Bayes.
  • Multinomial Naive BayesGaussian Naive Bayes; Decision trees; Support vector machine; Kernel trick; A comparison of methods; Regression problem; Classification problem; Hidden Markov model; A Python example; Summary; Web Mining Techniques; Web structure mining; Web crawlers (or spiders); Indexer; Ranking
  • PageRank algorithm; Web content mining; Parsing; Natural language processing; Information retrieval models; TF-IDF; Latent Semantic Analysis (LSA); Doc2Vec (word2vec); Word2vec
  • continuous bag of words and skip-gram architectures; Mathematical description of the CBOW model.
  • Doc2Vec extensionMovie review query example; Postprocessing information; Latent Dirichlet allocation; Model; Example; Opinion mining (sentiment analysis); Summary; Recommendation Systems; Utility matrix; Similarities measures; Collaborative Filtering methods; Memory-based Collaborative Filtering; User-based Collaborative Filtering; Item-based Collaborative Filtering; Simplest item-based Collaborative Filtering
  • slope one; Model-based Collaborative Filtering; Alternative least square (ALS); Stochastic gradient descent (SGD); Non-negative matrix factorization (NMF).
  • Singular value decomposition (SVD)CBF methods; Item features average method; Regularized linear regression method; Association rules for learning recommendation system; Log-likelihood ratios recommendation system method; Hybrid recommendation systems; Evaluation of the recommendation systems; Root mean square error (RMSE) evaluation; Classification metrics; Summary; Getting Started with Django; HTTP
  • the basics of the GET and POST methods; Installation and server creation; Settings; Writing an app
  • most important features; Models; URL and views behind HTML web pages; HTML pages.

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