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 discussed all the basic NLP techniques, starting with the definition of a corpus up to the final transformation into feature vectors. We analyzed different tokenizing methods to address particular problems or situations of splitting a document into words. Then, we introduced some filtering techniques that are necessary to remove all useless elements (also called stopwords) and to convert the inflected forms into standard tokens.

These steps are important to increase the information content by removing frequently used terms. When the documents have been successfully cleaned, it is possible to vectorize them using a simple approach such as the one implemented by the count-vectorizer, or a more complex one that takes into account the global distribution of terms, such as TF-IDF. The latter was introduced to complete the work done by the stemming phase...