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
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statisticschevron-right
    • Measuring Central Tendency
    • Probability
    • Standard Deviation , Variance
    • root mean squared error (RMSE)
    • mean Absolute Error
    • explained Variance
    • Coefficient of determination R2
    • Standard Error
    • Random Variablechevron-right
    • Sample vs Population
    • Normal Distribution
    • Z Score
    • Percentile
    • Skewness and Kurtosis
    • Co-variance vs Correlation
    • Confusion matrix
    • References
    • Types of datachevron-right
    • Bias versus variance trade-off
  • 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. Statistics

Skewness and Kurtosis

https://www.spcforexcel.com/knowledge/basic-statistics/are-skewness-and-kurtosis-useful-statisticsarrow-up-right

PreviousPercentilechevron-leftNextCo-variance vs Correlationchevron-right

Last updated 6 years ago

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