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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

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Colour book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

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Paul Pollock Nov 02, 2024

If you're a data scientist, ML engineer, or big data enthusiast looking to harness the power of Apache Spark for machine learning, this book is a must-read. Deepak Gowda brilliantly demystifies complex concepts, balancing theory with practical insights. From foundational machine learning principles to advanced techniques like clustering, recommendation systems, and deployment strategies, each chapter is packed with knowledge, well-structured examples, and actionable advice.One of the standout aspects of this book is its clarity. Even complex ideas like feature extraction and the inner workings of Spark MLlib are explained in a way that's easy to digest yet highly informative. The practical code snippets, especially for data ingestion and preprocessing, are invaluable and make it easy to follow along. Additionally, Gowda dives into real-world use cases, showing how Spark’s capabilities apply across industries like finance, healthcare, and e-commerce.For anyone seeking to scale ML applications efficiently or transition from traditional ML pipelines to Spark’s distributed architecture, this book is the perfect guide. It's not just a book—it's a comprehensive resource that bridges the gap between big data and machine learning with Apache Spark. Read more

Amazon Verified review

Amazon Customer Nov 28, 2024

The book’s strength lies in its detailed, recipe-driven approach, which simplifies Spark’s robust capabilities for processing and analyzing massive datasets. Book covers core machine learning concepts and seamlessly ties them to Spark’s advantages in big data analytics. The chapters on data preprocessing—especially feature extraction and transformation—are particularly noteworthy for their clarity and practical relevance.The structured exploration of supervised and unsupervised learning is another highlight. Chapters on regression, classification, clustering, and recommendation systems are not only theory-rich but also packed with practical coding examples. These examples, rooted in real-world applications, make complex algorithms accessible and demonstrate their utility across various domains.One of the book’s most useful aspects is its coverage of frequent pattern mining and strategies for deploying and optimizing machine learning models.By the conclusion, readers are equipped with the knowledge to efficiently preprocess, model, and deploy machine learning solutions using Apache Spark. The focus on practical application ensures that readers can immediately translate concepts into action. Read more

Apache Spark for Machine Learning: Build and deploy high-performance big data AI solutions for large-scale clusters

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