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.
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.
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