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MachineLearningfortheWeb
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Generalized linear models

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2022-01-24 10:26:12
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  • Machine Learning for the Web
    • Table of Contents
    • Machine Learning for the Web
    • Credits
    • Foreword
    • About the Author
    • About the Reviewers
    • www.PacktPub.com
    • Preface
      • What you need for this book
      • Who this book is for
      • Conventions
      • Reader feedback
      • Customer support
    • 1. Introduction to Practical Machine Learning Using Python
      • Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
      • Scientific libraries used in the book
      • When to use machine learning
      • Summary
    • 2. Unsupervised Machine Learning
      • Dimensionality reduction
      • Singular value decomposition
      • Summary
    • 3. Supervised Machine Learning
      • Generalized linear models
      • Naive Bayes
      • Decision trees
      • Support vector machine
      • A comparison of methods
      • Hidden Markov model
      • Summary
    • 4. Web Mining Techniques
      • Web content mining
      • Natural language processing
      • Postprocessing information
      • Summary
    • 5. Recommendation Systems
      • Similarities measures
      • Collaborative Filtering methods
      • CBF methods
      • Association rules for learning recommendation system
      • Log-likelihood ratios recommendation system method
      • Hybrid recommendation systems
      • Evaluation of the recommendation systems
      • Summary
    • 6. Getting Started with Django
      • Writing an app – most important features
      • Admin
      • Summary
    • 7. Movie Recommendation System Web Application
      • Models
      • Commands
      • User sign up login/logout implementation
      • Information retrieval system (movies query)
      • Rating system
      • Recommendation systems
      • Admin interface and API
      • Summary
    • 8. Sentiment Analyser Application for Movie Reviews
      • Search engine choice and the application code
      • Scrapy setup and the application code
      • Django models
      • Integrating Django with Scrapy
      • PageRank: Django view and the algorithm code
      • Admin and API
      • Summary
    • Index
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