“…Over 70 recipes to help you use Apache Spark as your single big data computing platform and master its libraries About This Book This book contains recipes on how to use Apache Spark as a unified compute engine Cover how to connect various source systems to Apache Spark Covers various parts of machine learning including supervised/unsupervised learning & recommendation engines Who This Book Is For This book is for data engineers, data scientists, and those who want to implement Spark for real-time data processing. …”

Tabla de Contenidos: “…Table of Contents Distributed Computing Primer Data Ingestion Data Cleansing and Integration Real-time Data Analytics Scalable Machine Learning with PySpark Feature Engineering – Extraction, Transformation, and Selection Supervised Machine Learning Unsupervised Machine Learning Machine Learning Life Cycle Management Scaling Out Single-Node Machine Learning Using PySpark Data Visualization with PySpark Spark SQL Primer Integrating External Tools with Spark SQL The Data Lakehouse…”

Materias:

Materias:

Tabla de Contenidos: “…Chapter 1: An Easy Transition -- Chapter 2: Selecting Algorithms -- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark -- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark -- Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark -- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark -- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark -- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark -- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark -- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark -- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark -- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark -- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark -- Chapter 17: Pipelines with Scikit-Learn and PySpark -- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark. …”

Tabla de Contenidos: “…Front matter -- Introduction -- First steps in PySpark -- Reading data into a data frame with spark read…”

Materias: “…Spark (Electronic resource : Apache Software Foundation)…”

Materias: “…Spark (Electronic resource : Apache Software Foundation)…”

Tabla de Contenidos: “…Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to Spark and Spark Mllib -- Chapter 3: Supervised Learning -- Chapter 4: Unsupervised Learning -- Chapter 5: Recommendations -- Chapter 6: Graph Analysis -- Chapter 7: Deep Learning…”

Tabla de Contenidos: “…Getting started -- Natural language basics -- NLP on Apache Spark -- Deep learning basics -- Part 2. Building blocks. …”

Materias: “…Spark (Electronic resource : Apache Software Foundation)…”

“…Building Flex Four app with Spark components…”

“…Skinning Spark components in Flex Four applications…”

Materias: “…Spark, Muriel 1918-2006 Crítica e interpretación…”