Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)

Summary

In this chapter, we exposed the generic Naive Bayes approach, starting from the Bayes' theorem and its intrinsic philosophy. The naiveness of such algorithms is due to the choice to assume all the causes to be conditional independent. This means that each contribution is the same in every combination and the presence of a specific cause cannot alter the probability of the other ones. This is often unrealistic; however, under some assumptions, it's possible to show that internal dependencies clear one another so that the resulting probability appears unaffected by their relations.

scikit-learn provides three Naive Bayes implementations: Bernoulli, Multinomial, and Gaussian. The only difference between them is in the probability distribution adopted. The first one is a binary algorithm, which is particularly useful when a feature can be present or not. Multinomial...