Data science from scratch : first principles with Python /

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you'll learn how many of the most fundamental data science tool...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Grus, Joel (Software engineer) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, [2019]
Edición:Second edition.
Temas:
Python (Computer program language)
Database management.
Data structures (Computer science)
Data mining.
Data mining > Mathematics.
Data Mining
Python (Langage de programmation)
Bases de données > Gestion.
Structures de données (Informatique)
Exploration de données (Informatique)
Exploration de données (Informatique) > Mathématiques.
COMPUTERS > Programming Languages > Python.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Introduction
  • A crash course in Python
  • Visualizing data
  • Linear algebra
  • Statistics
  • Probability
  • Hypothesis and inference
  • Gradient descent
  • Getting data
  • Working with data
  • Machine learning
  • k-Nearest neighbors
  • Naive bayes
  • Simple linear regression
  • Multiple regression
  • Logistic regression
  • Decision trees
  • Neural networks
  • Deep learning
  • Clustering
  • Natural language processing
  • Network analysis
  • Recommender systems
  • Databases and SQL
  • MapReduce
  • Data ethics
  • Go forth and do data science.

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