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Machine Learning
Part 13 of 20
The aim of the series is to consolidate a foundational understanding of Machine Learning.
Section 9: Decision Trees
Sections 9 and 10 are on tree-based methods. There are three main methods: Decision Trees (Section 9) Random Forests (Section 10) Boosted Trees (Section 10) Each of these methods stems from the basic decision tree algorithm. Fundamentally, tree-b...
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Section 15: Principal Component Analysis (PCA)
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Section 14: Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
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Section 13: Hierarchical Clustering
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Section 12: K-Means Clustering
So far we have done supervised learning. The remaining sections will be on unsupervised learning. Below is a quick guide on how to pick the estimator: Source: scikit-learn Unsupervised Learning: (1) Clustering: Using features, group together data r...
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Nur Fadhilah
21 posts