Course Outline

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

  • Machine Learning Languages, Types, and Examples
  • Supervised vs Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Model Evaluation

Machine Learning with Python

  • 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

Neural networks

  • Layers and nodes
  • Python neural network libraries
  • Working with scikit-learn
  • Working with PyBrain
  • Deep Learning

Requirements

Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.

 28 Hours

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