Machine learning, the study of software that learns from experience, has been successfully applied to many problems in recent years. In short, the machine learning technique has been able to solve many unsolved problems by generalizing the data and drawing conclusions from it. This has greatly reduced the efforts and time that a manual program would have demanded. The scikit-learn library for Python exposes the state-of-the-art implementations of many machine learning algorithms through a versatile interface.
This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. This book begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.
The book will then take you through fitting a linear model using a stochastic gradient descent and you will explore the differences between ordinary least squares, ridge, lasso, and elastic net regression. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through active learning and label propagation through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.
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About the Author
Gavin Hackeling is a software developer from New York. He studied at the University of North Carolina and received his master's degree from New York University. He currently works for an advertising technology company where he applies machine learning to problems including document classification.