Machine Learning
Ctrlk
  • 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?
  • Learning
    • Supervised Learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
    • Learning Means What
    • Goal
    • evaluation metrics
      • Regression
      • Model Validation
      • The bias, variance, and regularization properties
        • Regularization
          • Ridge regression
        • Bias And Variance
      • The key metrics to focus
    • hyperparameters
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statistics
  • Spark MLib
  • Terminology
  • Machine Learning Steps
  • Preprocessing and Feature selection techniues
  • The importance of variables feature selection/attribute selection
  • 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?
  • OverView
  • Data scaling and normalization
  • Questions
  • Which machine learning algorithm should I use?
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  1. Learning
  2. evaluation metrics
  3. The bias, variance, and regularization properties

Regularization

Ridge regression
PreviousThe bias, variance, and regularization propertiesNextRidge regression

Last updated 5 years ago

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