The user-item rating matrix is usually a huge matrix. The one we got from our dataset here comprises 30,114 rows and 19,228 columns, and most of the values in this matrix (99.999%) are zeros. This is expected. Say you own a streaming service with thousands of movies in your library. It is very unlikely that a user will watch more than a few dozen of them. This sparsity creates another problem. If one user watched the movie The Hangover: Part 1 and another user watched The Hangover: Part 2, from the matrix's point of view, they watched two different movies. We already know that collaborative filtering algorithms don't use users or item features. Thus, it is not aware of the fact that the two parts of The Hangover movie belong to the same franchise, let alone knowing that they both are comedies. To deal with this shortcoming, we need to transform our user-item rating matrix. We want the new matrix, or matrices, to be smaller and to capture...
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
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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.
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Current Title:
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
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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