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Book Overview & Buying
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Table Of Contents
Apache Spark for Machine Learning
By :
Apache Spark for Machine Learning
By:
Overview of this book
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.
Table of Contents (16 chapters)
Preface
Chapter 1: An Overview of Machine Learning Concepts
Chapter 2: Data Processing with Spark
Chapter 3: Feature Extraction and Transformation
Part 2: Supervised Learning
Chapter 4: Building a Regression System
Chapter 5: Building a Classification System
Part 3: Unsupervised Learning
Chapter 6: Building a Clustering System
Chapter 7: Building a Recommendation System
Chapter 8: Mining Frequent Patterns
Part 4: Model Deployment
Chapter 9: Deploying a Model
Index
