Apache Spark MLlib Training Course

spark.mllib: data types, algorithms, and utilities

• Data types
• Basic statistics
  • summary statistics
  • correlations
  • stratified sampling
  • hypothesis testing
  • streaming significance testing
  • random data generation
• Classification and regression
  • linear models (SVMs, logistic regression, linear regression)
  • naive Bayes
  • decision trees
  • ensembles of trees (Random Forests and Gradient-Boosted Trees)
  • isotonic regression
• Collaborative filtering
  • alternating least squares (ALS)
• Clustering
  • k-means
  • Gaussian mixture
  • power iteration clustering (PIC)
  • latent Dirichlet allocation (LDA)
  • bisecting k-means
  • streaming k-means
• Dimensionality reduction
  • singular value decomposition (SVD)
  • principal component analysis (PCA)
• Feature extraction and transformation
• Frequent pattern mining
  • FP-growth
  • association rules
  • PrefixSpan
• Evaluation metrics
• PMML model export
• Optimization (developer)
  • stochastic gradient descent
  • limited-memory BFGS (L-BFGS)

spark.ml: high-level APIs for ML pipelines

• Overview: estimators, transformers and pipelines
• Extracting, transforming and selecting features
• Classification and regression
• Clustering
• Advanced topics