Natural Language Processing (NLP) is a set of machine learning techniques that allow working with text documents, considering their internal structure, and the distribution of words. In this chapter, we're going to discuss all common methods to collect texts, split them into atoms, and transform them into numerical vectors. In particular, we'll compare different methods to tokenize documents (separate each word), to filter them, to apply special transformations to avoid inflected or conjugated forms, and finally to build a common vocabulary. Using the vocabulary, it will be possible to apply different vectorization approaches, to build feature vectors that can easily be used for classification or clustering purposes. To show you how to implement the whole pipeline, at the end of the chapter, we're going to set up a simple...
Machine Learning Algorithms - Second Edition
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.
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Current Title:
Machine Learning Algorithms - Second Edition
Table of Contents (19 chapters)
Preface
Free Chapter
A Gentle Introduction to Machine Learning
Important Elements in Machine Learning
Feature Selection and Feature Engineering
Regression Algorithms
Linear Classification Algorithms
Naive Bayes and Discriminant Analysis
Support Vector Machines
Decision Trees and Ensemble Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering
Introducing Recommendation Systems
Introducing Natural Language Processing
Topic Modeling and Sentiment Analysis in NLP
Introducing Neural Networks
Advanced Deep Learning Models
Creating a Machine Learning Architecture
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