Machine learning models, explained

A quick post to remind myself of this article: All Machine Learning Models Explained in 6 Minutes (2020).

Here is an outline:

  • Supervised learning
    • Regression
      • Linear regression
      • Decision tree
      • Random forest
      • Neural network
    • Classification
      • Logistic regression
      • Support vector machine
      • Naive Bayes
  • Unsupervised learning
    • Clustering: “Techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering”
    • Dimensionality reduction: “Most dimensionality-reduction techniques can be categorized as either feature elimination or feature extraction”

Reinforcement learning is not mentioned in the post.

To get your hands dirty with these models, look at scikit-learn — a Python library.

I also found this mildly interesting: The Machine Learning Process in 7 Steps (2021). It’s very brief.


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