Book Image

Mastering Machine Learning Algorithms

Book Image

Mastering Machine Learning Algorithms

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
13
Deep Belief Networks
Index

Spectral clustering


One of the most common problems of K-means and other similar algorithms is the assumption we have only hyperspherical clusters. This condition can be acceptable when the dataset is split into blobs that can be easily embedded into a regular geometric structure. However, it fails whenever the sets are not separable using regular shapes. Let's consider, for example, the following bidimensional dataset:

Sinusoidal dataset

As we are going to see in the example, any attempt to separate the upper sinusoid from the lower one using K-means will fail. The reason is quite obvious: a circle that contains the upper set will also contain part of the (or the whole) lower set. Considering the criterion adopted by K-means and imposing two clusters, the inertia will be minimized by a vertical separation corresponding to about x0 = 0. Therefore, the resulting clusters are completely mixed and only a dimension is contributing to the final configuration. However, the two sinusoidal sets are...