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
      • Evaluating supervised learning
      • Regression
        • Linear regression
        • Ridge regression
        • Least absolute shrinkage and selection operator (lasso) Regression
        • Polynomial regression
        • Performance Metrics
        • Regularization parameters in linear regression and ridge/lasso regression
        • Comments
      • Classification
      • Random Forest
      • Decision Trees
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
    • Learning Means What
    • Goal
    • evaluation metrics
    • 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?
Powered by GitBook
On this page

Was this helpful?

  1. Learning
  2. Supervised Learning

Regression

Linear regressionRidge regressionLeast absolute shrinkage and selection operator (lasso) RegressionPolynomial regressionPerformance MetricsRegularization parameters in linear regression and ridge/lasso regressionComments
PreviousUsing train/test to prevent overfitting of a polynomial regressionNextLinear regression

Last updated 6 years ago

Was this helpful?