In this chapter, we are going to start by looking at our first supervised learning algorithm—decision trees. The decision tree algorithm is versatile and easy to understand. It is widely used and also serves as a building block for the numerous advanced algorithms that we will encounter later on in this book. In this chapter, we will learn how to train a decision tree and use it for either classification or regression problems. We will also understand the details of its learning process in order to know how to set its different hyperparameters. Furthermore, we will use a real-world dataset to apply what we are going to learn here in practice. We will start by getting and preparing the data and apply our algorithm to it. Along the way, we will also try to understand key machine learning concepts, such as cross-validation and model evaluation metrics. By the end of this chapter, you will have a...
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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