Apache Spark for Machine Learning

Develop your data science skills with Apache Spark to solve real-world problems for Fortune 500 companies using scalable algorithms on large cloud computing clusters Key Features: - Apply techniques to analyze big data and uncover valuable insights for machine learning - Learn to use cloud computing clusters for training machine learning models on large datasets - Discover practical strategies to overcome challenges in model training, deployment, and optimization - Purchase of the print or Kindle book includes a free PDF eBook Book Description: In the world of big data, efficiently processing and analyzing massive datasets for machine learning can be a daunting task. Written by Deepak Gowda, a data scientist with over a decade of experience and 30+ patents, this book provides a hands-on guide to mastering Spark's capabilities for efficient data processing, model building, and optimization. With Deepak's expertise across industries such as supply chain, cybersecurity, and data center infrastructure, he makes complex concepts easy to follow through detailed recipes. This book takes you through core machine learning concepts, highlighting the advantages of Spark for big data analytics. It covers practical data preprocessing techniques, including feature extraction and transformation, supervised learning methods with detailed chapters on regression and classification, and unsupervised learning through clustering and recommendation systems. You'll also learn to identify frequent patterns in data and discover effective strategies to deploy and optimize your machine learning models. Each chapter features practical coding examples and real-world applications to equip you with the knowledge and skills needed to tackle complex machine learning tasks. By the end of this book, you'll be ready to handle big data and create advanced machine learning models with Apache Spark. What You Will Learn: - Master Apache Spark for efficient, large-scale data processing and analysis - Understand core machine learning concepts and their applications with Spark - Implement data preprocessing techniques for feature extraction and transformation - Explore supervised learning methods - regression and classification algorithms - Apply unsupervised learning for clustering tasks and recommendation systems - Discover frequent pattern mining techniques to uncover data trends Who this book is for: This book is ideal for data scientists, ML engineers, data engineers, students, and researchers who want to deepen their knowledge of Apache Spark's tools and algorithms. It's a must-have for those struggling to scale models for real-world problems and a valuable resource for preparing for interviews at Fortune 500 companies, focusing on large dataset analysis, model training, and deployment. Table of Contents - An Overview of Machine Learning Concepts - Data Processing with Spark - Feature Extraction and Transformation - Building a Regression System - Building a Classification System - Building a Clustering System - Building a Recommendation System - Mining Frequent Patterns - Deploying a Model