Learn PySpark

Learn PySpark
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

Covers entire range of PySpark's offerings from streaming to graph analytics Build standardized work flows for pre-processing and builds machine learning and deep learning models on big data sets Discusses how to schedule different Spark jobs using Airflow

Autorentext
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.

Inhalt
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.