Learn PySpark

Build Python-based Machine Learning and Deep Learning Models
1. Auflage 2019
ISBN: 978-1-4842-4960-4
Verlag: Apress

Buch, Englisch, 210 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 353 g

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges.
You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. 
You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.What You'll Learn

• Develop pipelines for streaming data processing using PySpark 
  
• Build Machine Learning & Deep Learning models using PySpark latest offerings
  
• Use graph analytics using PySpark 
  
• Create Sequence Embeddings from Text data 
  

Who This Book is For 

Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Singh, Pramod

Fachgebiete

• Mathematik | Informatik EDV | Informatik Betriebssysteme Linux Betriebssysteme, Open Source Betriebssysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Chapter 1: Introduction to PySpark.- Chapter 2: Data Processing.- Chapter 3: Spark Structured Streaming.- Chapter 4: Airflow.- Chapter 5: Machine Learning Library (MLlib).- Chapter 6: Supervised Machine Learning.- Chapter 7: Unsupervised Machine Learning.- Chapter 8: Deep Learning Using PySpark.

Über Autor(innen)

Pramod Singh is currently a Manager (Data Science) at Publicis Sapient and working as data science lead for a project with Mercedes Benz. He has spent the last nine years working on multiple Data projects at SapientRazorfish, Infosys & Tally and has used traditional to advanced machine learning and deep learning techniques in multiple projects using R, Python, Spark and Tensorflow. Pramod has also been a regular speaker at major conferences in India and abroad and is currently authoring a couple of books on Deep Learning and AI techniques. He regularly conducts Data Science meetups at SapientRazorfish and presents webinars on Machine Learning and Artificial Intelligence. He lives in Bangalore with his wife and 2-year-old son. In his spare time, he enjoys coding, reading and watching football.