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Data Science and Predictive Analytics Biomedical and Health Applications using R /

Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics cov...

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Bibliografske podrobnosti
Glavni avtor: Dinov, Ivo D. (Author)
Korporativna značnica: SpringerLink (Online service)
Format: Elektronski eKnjiga
Jezik:angleščina
Izdano: Cham : Springer International Publishing : Imprint: Springer, 2023.
Izdaja:2nd ed. 2023.
Serija:The Springer Series in Applied Machine Learning,
Teme:
Artificial intelligence > Data processing.
Quantitative research.
Machine learning.
Medical informatics.
Big data.
Data mining.
Data Science.
Data Analysis and Big Data.
Machine Learning.
Health Informatics.
Big Data.
Data Mining and Knowledge Discovery.
Online dostop:https://doi.org/10.1007/978-3-031-17483-4
Oznake: Označite
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Kazalo:
  • Chapter 1 - Introduction
  • Chapter 2: Basic Visualization and Exploratory Data Analytics
  • Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling
  • Chapter 4: Linear and Nonlinear Dimensionality Reduction
  • Chapter 5: Supervised Classification
  • Chapter 6: Black Box Machine Learning Methods
  • Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning
  • Chapter 8: Unsupervised Clustering
  • Chapter 9: Model Performance Assessment, Validation, and Improvement
  • Chapter 10: Specialized Machine Learning Topics
  • Chapter 11: Variable Importance and Feature Selection
  • Chapter 12: Big Longitudinal Data Analysis
  • Chapter 13: Function Optimization
  • Chapter 14: Deep Learning, Neural Networks.

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