| ISBN13: | 9798868808197 |
| ISBN10: | 8868808196 |
| Kötéstípus: | Puhakötés |
| Terjedelem: | 390 oldal |
| Méret: | 254x178 mm |
| Nyelv: | angol |
| Illusztrációk: | 59 Illustrations, black & white; 133 Illustrations, color |
| 700 |
Applied Data Science Using PySpark
EUR 64.19
Kattintson ide a feliratkozáshoz
This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade.
In Chapters 1, 2 & 3, we will get started with setting up the environment, and the basics of PySpark focusing on data manipulations. In Chapter 4, we will dive into the art of Variable Selection where we demonstrate various selection techniques available in PySpark. In Chapters 5, 6 & 7, we take you on the journey of machine learning algorithms, implementations and fine-tuning techniques. Chapters 8 and 9 will walk you through machine learning pipelines, and various methods available to operationalize the model and serve it through docker/API. Chapter 10 will demonstrate how you can unlock the power of predictive models when used in coherence to create a meaningful impact on your business. Chapter 11 will introduce you to some of the most used and powerful modelling frameworks to unlock real value from data.
In this new edition, you will learn predictive modelling frameworks that can quantify customer lifetime values and estimate the return of your predictive modelling investments. This edition also contains methods to measure engagement and identify actionable populations for churn treatments effectively. In addition, a dedicated chapter for experimentation design including steps to efficiently design, conduct, test and measure the results of your models is added. All the codes will be refreshed as needed to reflect the latest stable version of Spark.
You will:
- Learn the overview of end to end predictive model building
- Understand Multiple variable selection techniques & implementations
- Work with Operationalizing models
- Perform Data science experimentations & tips
This comprehensive guide, featuring hand-picked examples of daily use cases, will walk you through the end-to-end predictive model-building cycle using the latest techniques and industry tricks. In Chapters 1, 2, and 3, we will begin by setting up the environment and covering the basics of PySpark, focusing on data manipulation. Chapter 4 delves into the art of variable selection, demonstrating various techniques available in PySpark. In Chapters 5, 6, and 7, we explore machine learning algorithms, their implementations, and fine-tuning techniques. Chapters 8 and 9 will guide you through machine learning pipelines and various methods to operationalize and serve models using Docker/API. Chapter 10 will demonstrate how to unlock the power of predictive models to create a meaningful impact on your business. Chapter 11 introduces some of the most widely used and powerful modeling frameworks to unlock real value from data.
In this new edition, you will learn predictive modeling frameworks that can quantify customer lifetime values and estimate the return on your predictive modeling investments. This edition also includes methods to measure engagement and identify actionable populations for effective churn treatments. Additionally, a dedicated chapter on experimentation design has been added, covering steps to efficiently design, conduct, test, and measure the results of your models. All code examples have been updated to reflect the latest stable version of Spark.
You will:
- Gain an overview of end-to-end predictive model building
- Understand multiple variable selection techniques and their implementations
- Learn how to operationalize models
- Perform data science experiments and learn useful tips
Chapter 1: Setting up the Pyspark Environment.- Chapter 2: PySpark Basics .- Chapter 3: Variable Selection.- 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.- Chapter 10: Experimentation.- Chapter 11: Modeling Frameworks.