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Author:   Tshepo Chris Nokeri
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   0.274kg
ISBN:  

9781484277614

ISBN 10:   1484277619
Pages:   119
Publication Date:   26 October 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Chapter 1: Understanding Machine Learning and Deep Learning. Chapter goal: It carefully presents supervised and unsupervised ML and DL models and their application in the real world.  Understanding Machine Learning. Supervised Learning. The Parametric Method. The Non-parametric method. Ensemble Methods. Unsupervised Learning. Cluster Analysis. Dimension Reduction. Exploring Deep Learning. Conclusion. Chapter 2: Big Data Frameworks and ML and DL Frameworks. Chapter goal: It explains a big data framework recognized as PySpark, machine learning frameworks like SciKit-Learn, XGBoost, and H2O, and a deep learning framework called Keras.  Big Data Frameworks and ML and DL Frameworks. Big Data. Characteristics of Big Data. Impact of Big Data on Business and People. Better Customer Relationships. Refined Product Development. Improved Decision-Making. Big Data Warehousing. Big Data ETL. Big Data Frameworks. Apache Spark. Resilient Distributed Datasets. Spark Configuration. Spark Frameworks. ML Frameworks. SciKit-Learn. H2O. XGBoost. DL Frameworks. Keras. Conclusion. Chapter 3: The Parametric Method – Linear Regression. Chapter goal: It considers the most popular parametric model – the Generalized Linear Model. Regression Analysis. Regression in practice. SciKit-Learn in action. Spark MLlib in action. H2O in action. Conclusion. Chapter 4: Survival Regression Analysis. Chapter goal: It covers two main survival regression analysis models, the Cox Proportional Hazards and Accelerated Failure Time model. Cox Proportional Hazards. Lifeline in action. Accelerated Failure Time (AFT) model. Spark MLlib in Action. Conclusion. Chapter 5: The Non-Parametric Method - Classification. Chapter goal: It covers a binary classification model, recognized as Logistic Regression, using SciKit-Learn, Keras, PySpark MLlib, and H2O. Logistic Regression. Logistic Regression in Practice. SciKit-Learn in action. Spark MLlib in Action. H2O in action. Conclusion. Chapter 6: Tree-based Modelling and Gradient Boosting. Chapter goal: It covers two main ensemble methods, the decision tree model and the gradient boost model. Decision Tree. SciKit-Learn in action. Gradient Boosting. XGBoost in action. Spark MLlib in Action. H2O in action. Conclusion. Chapter 7: Artificial Neural Networks. Chapter goal: It covers deep learning and its application in the real world. It shows ways of designing, building, and testing an MLP classifier using the SciKit-Learn framework and an artificial neural network using the Keras framework.  Deep Learning. Restricted Boltzmann Machine. Multi-Layer Perception Neural Network. SciKit-Learn in action. Deep Belief Networks. Keras in action. H2O in action. Conclusion. Chapter 8: Cluster Analysis using K-Means. Chapter goal: It covers a technique of finding k, modelling and evaluating a cluster model known as K-Means using frameworks like SciKit-Learn, PySpark MLlib and H2O. K-Means. K-Mean in practice. SciKit-Learn in action. Spark MLlib in Action. H2O in action. Conclusion. Chapter 9: Dimension Reduction – Principal Components Analysis. Chapter goal: It covers a technique for reducing data into few components using the Principal Components Analysis.  Principal Components Analysis. SciKit-Learn in action. Spark MLlib in Action. H2O in Action. Conclusion. Chapter 10: Automated Machine Learning. Chapter goal: Acquaint the reader with the H2O AutoML model. Automated Machine Learning. H2O in Action. Conclusions.

Reviews

The book has a reader-centric style. Topics are covered briefly. ... The book can be considered as an introduction to various topics. Code listings and graphical results for different models are added benefits, which could enhance learning and exposure. (Jawwad Shamsi, Computing Reviews, June 29, 2022)

Author Information

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.

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