Introduction to Machine Learning

This course is designed for beginners to step into the field of machine learning. It introduces traditional regression and classification algorithms as well as their implementation in Python/R and interpretation for getting actionable business insight. By the end of the course, you will be able to distinguish between supervised and unsupervised learning algorithms, choose between regression and classification models as well as apply appropriate techniques to evaluate your results.

Curriculum

  1. Introduction to Machine Learning
  2. Supervised Learning: linear regression
  3. Model Evaluation, Diagnostics and Interpretation
  4. Supervised Learning: logistic regression
  5. Model Evaluation, Diagnostics and Interpretation
  6. Supervised Learning: CART
  7. Supervised Learning: tree based ensembles
  8. Unsupervised Learning: clustering algorithms
  9. Advanced Topics in Machine Learning

Prerequisites

  • Probability theory and calculus
  • Previous experience with Python/R is a plus

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