Learning Machine Learning, Part 2: Algorithms and Techniques

The previous blog post, Introduction to Machine Learning, presented the Machine Learning concept. Now, let’s discuss representative methods used in the technology.

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Regression Algorithms

In most Machine Learning courses, regression algorithms are the first to be introduced for two reasons:

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Neural Networks

Neural network algorithms (also called artificial neural networks or ANN) are used widely in deep learning, neural networks have returned. They are

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Support vector machine (SVM)

Support vector machine algorithms originated from the statistical learning field and came into their own as a classic algorithm in the ML field.
In a sense, SVM algorithms are enhancements of logistic regression algorithms. By providing logistic regression algorithms with stricter optimization

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Clustering Algorithms

For all the previously mentioned algorithms, one prominent characteristic is that the training data contains tags and trained models can predict tags

Descending Dimension Algorithms

A descending dimension algorithm is a type of unsupervised algorithm. It reduces data from a high-dimensional level to a low-dimensional level.

Recommendation Algorithms

Recommendation algorithms are very popular in the ML industry and widely used by e-commerce companies, such as Amazon, Tmall, and Jingdong.

Others

In addition to the algorithms previously mentioned, many others are used in Machine Learning, such as Gaussian discriminant, naive Bayes, and

  • Unsupervised algorithms (without tags): clustering algorithms and descending dimension algorithms
  • Special algorithms: recommendation algorithms

Conclusion

In this blog post, we learned about algorithms that help implement ML functions. These include regression algorithms, neural networks, SVM,

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