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Data science solutions with Python =...

Nokeri, Tshepo Chris.

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Data science solutions with Python = fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science solutions with Python/ by Tshepo Chris Nokeri.
Reminder of title:	fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
Author:	Nokeri, Tshepo Chris.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xvi, 119 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Understanding Machine Learning and Deep Learning -- Chapter 2: Big Data Frameworks and ML and DL Frameworks -- Chapter 3: The Parametric Method - Linear Regression -- Chapter 4: Survival Regression Analysis -- Chapter 5:The Non-Parametric Method - Classification -- Chapter 6:Tree-based Modelling and Gradient Boosting -- Chapter 7: Artificial Neural Networks -- Chapter 8: Cluster Analysis using K-Means -- Chapter 9: Dimension Reduction - Principal Components Analysis -- Chapter 10: Automated Machine Learning.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-7762-1
ISBN:	9781484277621

Data science solutions with Python = fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
Nokeri, Tshepo Chris.

Data science solutions with Pythonfast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /[electronic resource] :by Tshepo Chris Nokeri. - Berkeley, CA :Apress :2022. - xvi, 119 p. :ill., digital ;24 cm.

Chapter 1: Understanding Machine Learning and Deep Learning -- Chapter 2: Big Data Frameworks and ML and DL Frameworks -- Chapter 3: The Parametric Method - Linear Regression -- Chapter 4: Survival Regression Analysis -- Chapter 5:The Non-Parametric Method - Classification -- Chapter 6:Tree-based Modelling and Gradient Boosting -- Chapter 7: Artificial Neural Networks -- Chapter 8: Cluster Analysis using K-Means -- Chapter 9: Dimension Reduction - Principal Components Analysis -- Chapter 10: Automated Machine Learning.

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.

ISBN: 9781484277621

Standard No.: 10.1007/978-1-4842-7762-1doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Data science solutions with Python = fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
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245 1 0 $a Data science solutions with Python $h [electronic resource] : $b fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn / $c by Tshepo Chris Nokeri.
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505 0 $a Chapter 1: Understanding Machine Learning and Deep Learning -- Chapter 2: Big Data Frameworks and ML and DL Frameworks -- Chapter 3: The Parametric Method - Linear Regression -- Chapter 4: Survival Regression Analysis -- Chapter 5:The Non-Parametric Method - Classification -- Chapter 6:Tree-based Modelling and Gradient Boosting -- Chapter 7: Artificial Neural Networks -- Chapter 8: Cluster Analysis using K-Means -- Chapter 9: Dimension Reduction - Principal Components Analysis -- Chapter 10: Automated Machine Learning.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Python (Computer program language) $3 729789
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650 2 4 $a Artificial Intelligence. $3 769149
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856 4 0 $u https://doi.org/10.1007/978-1-4842-7762-1
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