文件名称:
Practical.Machine.Learning.178439968X
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文件大小: 16mb
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上传时间: 2016-02-25
详细说明： 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 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 ...展开收缩
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