You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Summary

In this chapter, we presented some fundamental clustering algorithms. We started with KNN, which is an instance-based method that restructures the dataset to find the most similar samples to a given query point. We discussed three approaches: a naive one, which is also the most expensive in terms of computational complexity, and two strategies based respectively on the construction of a k-d tree and a ball tree. These two data structures can dramatically improve performance even when the number of samples is very large.

The next topic was a classic algorithm: K-means, which is a symmetric partitioning strategy comparable to a Gaussian mixture with variances close to zero that can solve many real-life problems. We discussed both a vanilla algorithm, which couldn't find a valid sub-optimal solution, and an optimized initialization method, called K-means++, which was able to speed up the convergence toward solutions quite close to the global minimum.

We also presented...