Machine Learning
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  • Introduction
  • Self LEarning
  • Why Statstics in ML Or Data Science
  • How important is interpretability for a model in Machine Learning?
  • What are the most important machine learning techniques to master at this time?
  • Learningchevron-right
    • Supervised Learningchevron-right
    • Unsupervised learningchevron-right
    • Semi-supervised learning
    • Reinforcement learning
    • Learning Means What
    • Goal
    • evaluation metricschevron-right
      • Regressionchevron-right
      • Model Validationchevron-right
      • The bias, variance, and regularization propertieschevron-right
      • The key metrics to focus
    • hyperparameters
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statisticschevron-right
  • Spark MLibchevron-right
  • Terminology
  • Machine Learning Stepschevron-right
  • Preprocessing and Feature selection techniues
  • The importance of variables feature selection/attribute selectionchevron-right
  • Feature engineering
  • Hyperplanes
  • cross-validation
  • Machine learning losses
  • When to stop tuning machine learning models
  • Train, validation, and test data
  • input data structure
  • Why are matrices/vectors used in machine learning/data analysis?chevron-right
  • OverView
  • Data scaling and normalization
  • Questions
  • Which machine learning algorithm should I use?
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  1. Learningchevron-right
  2. evaluation metrics

The key metrics to focus

https://www.safaribooksonline.com/library/view/statistics-for-machine/9781788295758/bdd4260c-5a10-4db7-b195-ca13d97b9d0a.xhtmlarrow-up-right

PreviousBias And Variancechevron-leftNexthyperparameterschevron-right

Last updated 6 years ago

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