Apache Spark for Data Science Cookbook
Lingua: inglese
Editore: Packt Publishing, 2016
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Da: preigu, Osnabrück, Germaniapreigu
Venditore AbeBooks dal 5 agosto 2024
Condizione: Nuovo
EUR 64,35
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Apache Spark for Data Science Cookbook | Padma Priya Chitturi | Taschenbuch | Kartoniert / Broschiert | Englisch | 2016 | Packt Publishing | EAN 9781785880100 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Codice articolo 108554759
- Titolo
- Apache Spark for Data Science Cookbook
- Autore
- Padma Priya Chitturi
- Editore
- Packt Publishing
- Anno di pubblicazione
- 2016
- Condizione
- Neu
- Rilegatura
- Taschenbuch
- Lingua
- inglese
- ISBN 10
- 1785880101
- ISBN 13
- 9781785880100
- Peso dell'articolo
- 730 grammi
- Dimensioni
- 235 x 191 x 21 mm
- Cataloghi dei venditori
- Bücher
Key Features
- Use Apache Spark for data processing with these hands-on recipes
- Implement end-to-end, large-scale data analysis better than ever before
- Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data
Book Description
Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease.
This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
What you will learn
- Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.
- Solve real-world analytical problems with large data sets.
- Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale.
- Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package.
- Learn about numerical and scientific computing using NumPy and SciPy on Spark.
- Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models.
About the Author
Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey dean's work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
Table of Contents
- Big Data Analytics with Spark
- Tricky Statistics with Spark
- Data Analysis with Spark
- Clustering, Classification, and Regression
- Working with Spark MLlib
- NLP with Spark
- Working with Sparkling Water - H2O
- Data Visualization with Spark
- Deep Learning on Spark
- Working with SparkR
"Riassunto" può appartenere a un’altra edizione di questo titolo.
L'autore
Padma Priya Chitturi is a Senior Research and Development Engineer at Amadeus Software Labs (India) Pvt Ltd and has over 4 years of experience in Big Data processing. Currently, she is working for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus. She has worked on realizing large-scale deep networks (Jeffrey dean's work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm Cassandra, and Hadoop. She is an open source contributor to Apache Storm and you can find her name in the Storm community.
She has also authored technical and research articles.
"Descrizione articolo" può appartenere a un’altra edizione di questo titolo.
preigu
Osnabrück, Germania
Venditore AbeBooks dal 5 agosto 2024
Tariffe di spedizione da Germania a U.S.A.
| Articolo | Da 60 a 60 giorni lavorativi | Da 60 a 60 giorni lavorativi |
|---|---|---|
| Primo articolo | EUR 70,00 | EUR 70,00 |
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