In a recommendation task, you have a set of users interacting with a set of items and your job is to figure out which items are suitable for which users. You may know a thing or two about each user: where they live, how much they earn, whether they are logged in via their phone or their tablet, and more. Similarly, for an item—say, a movie—you know its genre, its production year, and how many Academy Awards it has won. Clearly, this looks like a classification problem. You can combine the user features with the item features and build a classifier for each user-item pair, and then try to predict whether the user will like the item or not. This approach is known as content-based filtering. As its name suggests, it is as good as the content or the features extracted from each user and each item. In practice, you may only know basic information about each user. A user's location or gender may reveal enough about their tastes...
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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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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