- Large Scale Machine Learning with Spark
- Large Scale Machine Learning with Spark
- Credits
- About the Authors
- About the Reviewer
- www.Packtpub.com
- Preface
- 1. Introduction to Data Analytics with Spark
- 2. Machine Learning Best Practices
- What is machine learning?
- Machine learning tasks
- Practical machine learning problems
- Most widely used machine learning problems
- Large scale machine learning APIs in Spark
- Practical machine learning best practices
- Best practice before developing an ML application
- Good machine learning and data science worth huge
- Best practice – feature engineering and algorithmic performance
- Beware of overfitting and underfitting
- Stay tuned and combining Spark MLlib with Spark ML
- Making ML applications modular and simplifying pipeline synthesis
- Thinking of an innovative ML system
- Thinking and becoming smarter about Big Data complexities
- Applying machine learning to dynamic data
- Best practice after developing an ML application
- Best practice before developing an ML application
- Choosing the right algorithm for your application
- Summary
- 3. Understanding the Problem by Understanding the Data
- 4. Extracting Knowledge through Feature Engineering
- The state of the art of feature engineering
- Best practices in feature engineering
- Feature engineering with Spark
- Advanced feature engineering
- Summary
- 5. Supervised and Unsupervised Learning by Examples
- 6. Building Scalable Machine Learning Pipelines
- Spark machine learning pipeline APIs
- Cancer-diagnosis pipeline with Spark
- Cancer-prognosis pipeline with Spark
- Market basket analysis with Spark Core
- OCR pipeline with Spark
- Topic modeling using Spark MLlib and ML
- Credit risk analysis pipeline with Spark
- Scaling the ML pipelines
- Tips and performance considerations
- Summary
- 7. Tuning Machine Learning Models
- Details about machine learning model tuning
- Typical challenges in model tuning
- Evaluating machine learning models
- Validation and evaluation techniques
- Parameter tuning for machine learning models
- Hypothesis testing
- Machine learning model selection
- Summary
- 8. Adapting Your Machine Learning Models
- 9. Advanced Machine Learning with Streaming and Graph Data
- 10. Configuring and Working with External Libraries
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Authors
Md. Rezaul Karim
Md. Mahedi Kaysar |
Copy Editor
Safis Editing |
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Reviewer
Muthukumar Subramanian |
Project Coordinator
Shweta H Birwatkar |
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Commissioning Editor
Akram Hussain |
Proofreader
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Acquisition Editor
Lester Frias |
Indexer
Aishwarya Gangawane |
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Content Development Editor
Amrita Noronha |
Graphics
Disha Haria |
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Technical Editor
Akash Patel |
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Arvindkumar Gupta |
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