Saturday Morning Video: Machine Learning with Scikit Learn, Andreas Mueller & Kyle Kastner, SciPy 2015 Tutorial

Beware, the two parts of this tutorial by Andreas Mueller and Kyle Kastner lasts 6 hours in all:

 

All material is here: https://github.com/amueller/scipy_2015_sklearn_tutorial

Outline

Morning Session

• What is machine learning? (Sample applications)
• Kinds of machine learning: unsupervised vs supervised.
• Data formats and preparation.
• Supervised learning
  • Interface
  • Training and test data
  • Classification
  • Regression
• Unsupervised Learning
  • Unsupervised transformers
  • Preprocessing and scaling
  • Dimensionality reduction
  • Clustering
• Summary : Estimator interface
• Application : Classification of digits
• Application : Eigenfaces
• Methods: Text feature abstraction, bag of words
• Application : SMS spam detection
• Summary : Model building and generalization

Afternoon Session

• Cross-Validation
• Model Complexity: Overfitting and underfitting
• Complexity of various model types
• Grid search for adjusting hyperparameters
• Basic regression with cross-validation
• Application : Titanic survival with Random Forest
• Building Pipelines
  • Motivation and Basics
  • Preprocessing and Classification
  • Grid-searching Parameters of the feature extraction
• Application : Image classification
• Model complexity, learning curves and validation curves
• In-Depth supervised models
  • Linear Models
  • Kernel SVMs
  • trees and Forests
• Learning with Big Data
  • Out-Of-Core learning
  • The hashing trick for large text corpuses

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