Introduction
  • Introduction
Part I - Types of learning in sklearn
  • Part 1 - Types of machine learning in sklearn
Part II - Types of supervised learning
  • Part II - Types of supervised learning
Part III - Types of semi-supervised learning
  • Part III - Types of semi-supervised learning
Part IV - Unsupervised learning in nsklearn (clustering)
  • Part IV - Unsupervised learning
Part V - Sklearn models for supervised learning
  • Part V(0) - Sklearn models for supervised learning
  • Part V(1) - Sklearn models for supervised learning
Part VI - Sklearn models for semi-supervised learning
  • Part VI - Sklearn models for semi-supervised learning
Part VII - Sklearn models for unsupervised learning
  • Part VII - Sklearn models for unsupervised learning
Part VIII - Dimensionality reduction in sklearn
  • Part VIII - Diminsionality reduction
Part IX - Feature selection in sklearn
  • Part IX - Sklearn feature selection
Part X - Preprocessing in sklearn
  • Part X - Preprocessing in sklearn
Part XI - Hyperparameter tuning in sklearn
  • Part XI - Hyperparameter tuning in sklearn
Part XII - Goodness of fit tests in sklearn
  • Part XII - Goodness of fit testing in sklearn
Bonus Lecture
  • Bonus Lecture