دانلود کتاب یادگیری ماشینی توزیع شده با PySpark

In recent years, the amount of data generated and collected by companies and organizations has grown exponentially. As a result, data scientists have been pushed to process and analyze large amounts of data, and traditional single-node computing tools such as Pandas and Scikit-Learn have become inadequate. In response, many data scientists have turned to distributed computing frameworks such as Apache Spark, with its Python-based interface, PySpark.
PySpark has several advantages over single-node computing, including the ability to handle large volumes of data and the potential for significantly faster data processing times. Furthermore, because PySpark is built on top of Spark, a widely used distributed computing framework, it also offers a broader set of tools for data processing and machine learning.
While transitioning from Pandas and Scikit-Learn to PySpark may seem daunting, the transition can be relatively straightforward. Pandas/Scikit-Learn and PySpark offer similar APIs, which means that many data scientists can easily transition from one to the other.
In this context, this book will explore the benefits of using PySpark over traditional single-node computing tools and provide guidance for data scientists who are considering transitioning to PySpark.
In this book, we aim to provide a comprehensive overview of the main machine learning algorithms with a particular focus on regression and classification. These are fundamental techniques that form the backbone of many practical applications of machine learning. We will cover popular methods such as linear and logistic regression, decision trees, random forests, gradient-boosted trees, support vector machines, Naive Bayes, and neural networks. We will also discuss how these algorithms can be applied to real-world problems, such as predicting house prices, and the likelihood of diabetes as well as classifying handwritten digits or the species of an Iris flower and predicting whether a tumor is benign or malignant. Whether you are a beginner or an experienced practitioner, this book is designed to help you understand the core concepts of machine learning and develop the skills needed to apply these methods in practice. This book spans 18 chapters and covers multiple topics. The first two chapters examine why migration from Pandas and Scikit-Learn to PySpark can be a seamless process, and address the challenges of selecting an algorithm. Chapters 3–6 build, train, and evaluate some popular regression models, namely, multiple linear regression, decision trees, random forests, and gradient-boosted trees, and use them to deal with some real-world tasks such as predicting house prices. Chapters 7–12 deal with classification issues by building, training, and evaluating widely used algorithms such as logistic regression, decision trees, random forests, support vector machines, Naive Bayes, and neural networks. In Chapters 13–15, we examine three additional types of algorithms, namely, recommender systems, natural language processing, and clustering with k-means. In the final three chapters, we deal with hyperparameter tuning, pipelines, and deploying models into production.