Fundamentals of Data Science Part III: Machine Learning

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In Part III of this series, we cover the fundamentals of machine learning, focusing on:

  • validation methodology (reprint)
  • nearest neighbor, k-means, support vector machines, principal component analysis
  • tree-based methods: decision trees, bagging, random forest, boosting, XGBoost
  • artificial neural networks and deep learning
  • reinforcement learning

The focus is on algorithmic development and programming. We code each technique from scratch in Python, using an object-oriented approach.

Cayenne Canyon Press