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• Introduction to Applied Machine Learning
  Statistical learning vs. Machine learning
  Iteration and evaluation
  Bias-Variance trade-off
  Machine Learning with Scala
  Choice of libraries
  Add-on tools
  Regression
  Linear regression
  Generalizations and Nonlinearity
  Exercises
  Classification
  Bayesian refresher
  Naive Bayes
  Logistic regression
  K-Nearest neighbors
  Exercises
  Cross-validation and Resampling
  Cross-validation approaches
  Bootstrap
  Exercises
  Unsupervised Learning
  K-means clustering
  Examples
  Challenges of unsupervised learning and beyond K-means