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 Duration 28 hours

Course Outline

Introduction to Applied Machine Learning

  • Distinctions between statistical learning and Machine Learning
  • Cycles of iteration and evaluation
  • Understanding the Bias-Variance trade-off

Supervised and Unsupervised Learning

  • Overview of Machine Learning languages, types, and case studies
  • Comparison of Supervised vs Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluating model performance

Implementing Machine Learning with Python

  • Selecting appropriate libraries
  • Utilizing supplementary tools

Regression

  • Linear regression
  • Handling generalizations and nonlinearity
  • Practical exercises

Classification

  • Review of Bayesian concepts
  • Naive Bayes
  • Logistic regression
  • K-Nearest Neighbors
  • Practical exercises

Cross-validation and Resampling

  • Various Cross-validation strategies
  • Bootstrap methods
  • Practical exercises

Unsupervised Learning

  • K-means clustering
  • Real-world examples
  • Challenges in unsupervised learning and methods beyond K-means

Neural Networks

  • Understanding layers and nodes
  • Python libraries for neural networks
  • Working with scikit-learn
  • Working with PyBrain
  • Deep Learning

Requirements

Proficiency in the Python programming language is required. A foundational understanding of statistics and linear algebra is also recommended.

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