Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn - Softcover

Nokeri, Tshepo Chris

Softcover

ISBN 10: 1484277619 ISBN 13: 9781484277614

Publisher: Apress, 2021

Synopsis

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. 

The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras.

The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.

This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. 

What You Will Learn

• Understand widespread supervised and unsupervised learning, including key dimension reduction techniques
• Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning
• Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks
• Design, build, test, and validate skilled machine models and deep learning models
• Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration

 

Who This Book Is For

Data scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics

About this title

About the Author

Tshepo Chris Nokeri harnesses advanced analytics and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor’s degree in information management. Afterward, he graduated with an Honours degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize.

From the Back Cover

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. 

The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras.

The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model(Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.

This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. 

What You Will Learn

• Understand widespread supervised and unsupervised learning, including key dimension reduction techniques
• Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning
• Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks
• Design, build, test, and validate skilled machine models and deep learning models
• Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration

 

Edition details

• Publisher: Apress
• Publication date: 2021
• Language: English
• ISBN 10: 1484277619
• ISBN 13: 9781484277614
• Binding: Paperback
• Edition number: 1
• Number of pages: 136