- eBook:Applied Data Science Using PySpark: Learn the End-to-End Predictive Model-Building Cycle
- Author:Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla
- Edition:1 edition
- Data:January 3, 2021
- Pages:436 pages
- Format:PDF, ePUB
Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines.
By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets.
What You Will Learn
- Build an end-to-end predictive model
- Implement multiple variable selection techniques
- Operationalize models
- Master multiple algorithms and implementations
Data scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data.
Chapter 2: PySpark Basics
Chapter 3: Utility Functions and Visualizations
Chapter 4: Variable Selection
Chapter 5: Supervised Learning Algorithms
Chapter 6: Model Evaluation
Chapter 7: Unsupervised Learning and Recommendation Algorithms
Chapter 8: Machine Learning Flow and Automated Pipelines
Chapter 9: Deploying Machine Learning Models