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.

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Spis treści

Apache Spark for Machine Learning. Build and deploy high-performance big data AI solutions for large-scale clusters eBook -- spis treści

• 1. An Overview of Machine Learning Concepts
• 2. Data Processing with Spark
• 3. Feature Extraction and Transformation
• 4. Building a Regression System
• 5. Building a Classification System
• 6. Building a Clustering System
• 7. Building a Recommendation System
• 8. Mining Frequent Patterns
• 9. Deploying a Model