The acronym DBSCAN stands for density-based spatial clustering of applications with noise. It sees clusters as areas of high density separated by areas of low density. This allows it to deal with clusters of any shape. This is in contrast to the K-means algorithm, which assumes clusters to be convex; that is, data blobs with centroids. The DBSCANalgorithm starts by identifying the core samples. These are points that have at least min_samples around them within a distance of eps (ε). Initially, a cluster is built out of its core samples. Once a core sample has been identified, its neighbors are also examined and added to the cluster if they meet the core sample criteria. Then, the cluster is expanded so that we can add non-core samples to it. These are samples that can be reached directly from the core samples within a distance...
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Table Of Contents
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
By :
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
By:
Overview of this book
Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
Table of Contents (18 chapters)
Preface
Section 1: Supervised Learning
Free Chapter
Introduction to Machine Learning
Making Decisions with Trees
Making Decisions with Linear Equations
Preparing Your Data
Image Processing with Nearest Neighbors
Classifying Text Using Naive Bayes
Section 2: Advanced Supervised Learning
Neural Networks – Here Comes Deep Learning
Ensembles – When One Model Is Not Enough
The Y is as Important as the X
Imbalanced Learning – Not Even 1% Win the Lottery
Section 3: Unsupervised Learning and More
Clustering – Making Sense of Unlabeled Data
Anomaly Detection – Finding Outliers in Data
Recommender System – Getting to Know Their Taste
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