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

Principal Component Analysis


Another common approach to the problem of reducing the dimensionality of a high-dimensional dataset is based on the assumption that, normally, the total variance is not explained equally by all components. If pdata is a multivariate Gaussian distribution with covariance matrix Σ, then the entropy (which is a measure of the amount of information contained in the distribution) is as follows:

Therefore, if some components have a very low variance, they also have a limited contribution to the entropy, providing little additional information. Hence, they can be removed without a high loss of accuracy.

Just as we've done with FA, let's consider a dataset drawn from pdata ∼ N(0, Σ) (for simplicity, we assume that it's zero-centered, even if it's not necessary):

Our goal is to define a linear transformation, z = ATx (a vector is normally considered a column, therefore x has a shape (n × 1)), such as the following:

As we want to find out the directions where the variance...