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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.
Covers how to transition from Python-based ML models to PySpark-based large scale models
Covers how to automate your data workflow using Airflow
Explains the end-to end machine learning pipeline for model prediction
Chapter 1: Introduction to Spark 3.1.- Chapter 2: Manage Data with PySpark.- Chapter 3: Introduction to Machine Learning.- Chapter 4: Linear Regression with PySpark.- Chapter 5: Logistic Regression with PySpark.- Chapter 6: Ensembling with PySpark.- Chapter 7: Clustering with PySpark.- Chapter 8: Recommendation Engine with PySpark.- Chapter 9: Advanced Feature Engineering with PySpark.
Erscheinungsjahr: | 2021 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xviii
220 S. 202 s/w Illustr. 220 p. 202 illus. |
ISBN-13: | 9781484277768 |
ISBN-10: | 1484277767 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Singh, Pramod |
Auflage: | 2nd edition |
Hersteller: | APRESS |
Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
Maße: | 254 x 178 x 14 mm |
Von/Mit: | Pramod Singh |
Erscheinungsdatum: | 09.12.2021 |
Gewicht: | 0,46 kg |
Covers how to transition from Python-based ML models to PySpark-based large scale models
Covers how to automate your data workflow using Airflow
Explains the end-to end machine learning pipeline for model prediction
Chapter 1: Introduction to Spark 3.1.- Chapter 2: Manage Data with PySpark.- Chapter 3: Introduction to Machine Learning.- Chapter 4: Linear Regression with PySpark.- Chapter 5: Logistic Regression with PySpark.- Chapter 6: Ensembling with PySpark.- Chapter 7: Clustering with PySpark.- Chapter 8: Recommendation Engine with PySpark.- Chapter 9: Advanced Feature Engineering with PySpark.
Erscheinungsjahr: | 2021 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xviii
220 S. 202 s/w Illustr. 220 p. 202 illus. |
ISBN-13: | 9781484277768 |
ISBN-10: | 1484277767 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Singh, Pramod |
Auflage: | 2nd edition |
Hersteller: | APRESS |
Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
Maße: | 254 x 178 x 14 mm |
Von/Mit: | Pramod Singh |
Erscheinungsdatum: | 09.12.2021 |
Gewicht: | 0,46 kg |