Practical Machine Learning

About This Book

• Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
• Comprehensive practical solutions taking you into the future of machine learning
• Go a step further and integrate your machine learning projects with Hadoop

Who This Book Is For

This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.

What You Will Learn

• Implement a wide range of algorithms and techniques for tackling complex data
• Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
• Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully
• Apply the appropriate Machine learning technique to address a real-world problem
• Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning
• Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more

In Detail

This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.

We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning’s real-world application.

The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies.

Table of Contents

Chapter 1. Introduction to Machine learning
Chapter 2. Machine learning and Large-scale datasets
Chapter 3. An Introduction to Hadoop’s Architecture and Ecosystem
Chapter 4. Machine Learning Tools, Libraries, and Frameworks
Chapter 5. Decision Tree based learning
Chapter 6. Instance and Kernel Methods Based Learning
Chapter 7. Association Rules based learning
Chapter 8. Clustering based learning
Chapter 9. Bayesian learning
Chapter 10. Regression based learning
Chapter 11. Deep learning
Chapter 12. Reinforcement learning
Chapter 13. Ensemble learning
Chapter 14. New generation data architectures for Machine learning