Description
by
Jared M. Maruskin (Author)
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.
Number of Pages: 316Dimensions: 0.66 x 9.21 x 6.14 INPublication Date: April 29, 2022
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