دانلود کتاب علم داده کاربردی با استفاده از PySpark

Discover the capabilities of Pyspark and its application in the realm of data science. 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 first 3 chapters, we will get you started with the setting up of the environment, basics of PySpark focusing on data manipulations. We understand feature engineering is where data science professionals spend 70% of their time. As we make you comfortable with the language, we build upon that to introduce you to the mathematical functions available off the shelf. Before we move to the next chapter we will introduce you to the predictive modeling framework. In Chapter 4, we will dive into the art of Variable Selection where we demonstrate various selection techniques available in PySpark. In Chapter 5, 6 & 7, we take you on the journey of machine learning algorithms, implementations and fine-tuning techniques. In addition, we will also talk about different validation metrics and how to use them for picking the best models. Chapter 8 and 9 will walk you through machine learning pipelines, various methods available to operationalize the model and serve it through docker/API. Chapter 10 includes some tricks that can help you optimize your programs and machine learning pipelines. You will learn the flexibility, advantages and become comfortable using PySpark in data science applications. This book is recommended to data science enthusiasts who want to unleash the power of parallel computing by simultaneously working with big datasets. Highly recommended for professionals who want to switch from traditional languages to open source in big data setting. It’s a value add for students who want to work on big data.

What you will learn:

• Overview of end to end predictive model building • Multiple variable selection techniques & implementations • Multiple algorithms & implementations • Operationalizing models • Data science experimentations & tips