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

Clustering and Unsupervised Models

In this chapter, we are going to introduce some fundamental clustering algorithms and discuss their strengths and weaknesses. The field of unsupervised learning, as well as any other machine learning approach, must always be based on the concept of Occam's razor. Simplicity must always be preferred, so long as the performance of the model meets your requirements.

However, in this case, the ground truth can be unknown. When a clustering algorithm is adopted as an exploratory tool, we can only assume that the dataset represents a precise data-generating process. If this assumption is correct, the best strategy is to determine the number of clusters to maximize the internal cohesion (denseness) and the external separation. This means that we expect to find blobs (or isles) whose samples share some common and partially unique features.

In particular, the algorithms and the topics we are going to analyze are: