Machine Learning Fundamentals with R培训

课程编码

MLFWR1

课程时长

14 小时 通常来说是2天,包括中间休息。

要求

Knowledge of R programming language. Basic familiarity with statistics and linear algebra is recommended.

课程概览

本课程的目的是提供在实践中应用Machine Learning方法的基本熟练程度。通过使用R编程平台及其各种库,并基于大量实际示例,本课程教授如何使用Machine Learning最重要的构建块,如何制定数据建模决策,解释算法的输出和验证结果。

我们的目标是让您自信地理解和使用Machine Learning工具箱中最基本的工具,并避免Data Science应用程序的常见缺陷。

Machine Translated

课程大纲

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Exercises

Classification

  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercises

Cross-validation and Resampling

  • Cross-validation approaches
  • Bootstrap
  • Exercises

Unsupervised Learning

  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means

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