Machine Learning with PySpark
- With Natural Language Processing and Recommender Systems
- Indbinding:
- Paperback
- Sideantal:
- 220
- Udgivet:
- 9. december 2021
- Udgave:
- 2
- Størrelse:
- 253x177x19 mm.
- Vægt:
- 468 g.
- 8-11 hverdage.
- 7. december 2024
På lager
Normalpris
Abonnementspris
- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Machine Learning with PySpark
Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems.
Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. Yoüll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. Yoüll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. Yoüll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark¿s latest ML library.
After completing this book, you will understand how to use PySpark¿s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications
What you will learn:
Build a spectrum of supervised and unsupervised machine learning algorithms
Use PySpark's machine learning library to implement machine learning and recommender systems
Leverage the new features in PySpark¿s machine learning library
Understand data processing using Koalas in Spark
Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models
Who This Book Is For
Data science and machine learning professionals.
Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. Yoüll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. Yoüll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. Yoüll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark¿s latest ML library.
After completing this book, you will understand how to use PySpark¿s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications
What you will learn:
Build a spectrum of supervised and unsupervised machine learning algorithms
Use PySpark's machine learning library to implement machine learning and recommender systems
Leverage the new features in PySpark¿s machine learning library
Understand data processing using Koalas in Spark
Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models
Who This Book Is For
Data science and machine learning professionals.
Brugerbedømmelser af Machine Learning with PySpark
Giv din bedømmelse
For at bedømme denne bog, skal du være logget ind.Andre købte også..
Find lignende bøger
Bogen Machine Learning with PySpark findes i følgende kategorier:
© 2024 Pling BØGER Registered company number: DK43351621