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

Semi-supervised scenario


A typical semi-supervised scenario is not very different from a supervised one. Let's suppose we have a data generating process, pdata:

However, contrary to a supervised approach, we have only a limited number N of samples drawn from pdata and provided with a label, as follows:

Instead, we have a larger amount (M) of unlabeled samples drawn from the marginal distribution p(x):

In general, there are no restrictions on the values of N and M; however, a semi-supervised problem arises when the number of unlabeled samples is much higher than the number of complete samples. If we can draw N >> M labeled samples from pdata, it's probably useless to keep on working with semi-supervised approaches and preferring classical supervised methods is likely to be the best choice. The extra complexity we need is justified by M >> N, which is a common condition in all those situations where the amount of available unlabeled data is large, while the number of correctly labeled...