A Practitioner's Guide to Machine Learning
Edition: 0
Copyright: 2020
Pages: 203
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A Practitioner's Guide to Machine Learning takes a narrative approach to introduce machine learning topics to the readers. The topics include Nearest Neighbors, Association Rules, Clustering, Decision Tree, Logistic Regression, Feature Selection, Naive Bayes, Neural Network, Support Vector Machines, Ensemble Model, and Gradient Boosting.
The publication helps readers formulate their ways to execute machine learning algorithms, evaluate the algorithms’ performances, and make evidence-based decisions. In addition, it raises the readers’ awareness of the strengths and limitations of various machine learning algorithms. Throughout the book, the author reminds the readers of the process of practicing machine learning. That includes understanding requirements, scoping the problem, acquiring data, preparing data, developing the program codes, retrieving the output, interpreting the results, and drawing the conclusions. Last and most importantly, check if the conclusions can satisfy the requirement or not.
To this end, the author structures each chapter with these goals in mind. At the beginning of each chapter, the author describes a business problem by telling the readers an everyday story. After using the business problem to lay out the argument for the need for the machine learning algorithm, a theory is then presented, a description of the implementation, some recommend open-source libraries, and walk through examples with code snippets. Finally, each chapter concludes with a self-reflection on the topic. In addition, readers can exercise their understanding of the machine learning topic at the end of each chapter.
Mr. Ming-Long Lam is a data science researcher, developer, and educator with more than twenty-eight years of data science practice in software development, property and casualty insurance, and retail financial service industries. Dr. Lam has strong expertise in developing statistical and machine learning algorithms, customizing solutions for analyzing person data, and implementing solutions as in major analytical software.
Dr. Lam is currently a Principal Research Statistician Developer at the SAS Institute, and on the faculty of the Department of Computer Science at the Illinois Institute of Technology and the Master of Science in Analytics program at the University of Chicago. Before joining the SAS Institute, Dr. Lam had a rewarding career in directing the development of analytical features in SPSS. In between these two major statistical software vendors, Allstate recruited Dr. Lam to sophisticate their insurance rating plans and to discover their most valuable customers. Dr. Lam also joined Chase to develop new performance assessment models for recruiting retail financial staff and for increasing their tenures at the bank.
Dr. Lam has co-published the book Using Data Analysis to Improve Student Learning: Toward 100% Proficiency. Dr. Lam earned his bachelor’s degree in mathematics and his master’s degree in statistics from the Chinese University of Hong Kong. Dr. Lam also earned his doctorate in statistics from the University of Chicago.
A Practitioner's Guide to Machine Learning takes a narrative approach to introduce machine learning topics to the readers. The topics include Nearest Neighbors, Association Rules, Clustering, Decision Tree, Logistic Regression, Feature Selection, Naive Bayes, Neural Network, Support Vector Machines, Ensemble Model, and Gradient Boosting.
The publication helps readers formulate their ways to execute machine learning algorithms, evaluate the algorithms’ performances, and make evidence-based decisions. In addition, it raises the readers’ awareness of the strengths and limitations of various machine learning algorithms. Throughout the book, the author reminds the readers of the process of practicing machine learning. That includes understanding requirements, scoping the problem, acquiring data, preparing data, developing the program codes, retrieving the output, interpreting the results, and drawing the conclusions. Last and most importantly, check if the conclusions can satisfy the requirement or not.
To this end, the author structures each chapter with these goals in mind. At the beginning of each chapter, the author describes a business problem by telling the readers an everyday story. After using the business problem to lay out the argument for the need for the machine learning algorithm, a theory is then presented, a description of the implementation, some recommend open-source libraries, and walk through examples with code snippets. Finally, each chapter concludes with a self-reflection on the topic. In addition, readers can exercise their understanding of the machine learning topic at the end of each chapter.
Mr. Ming-Long Lam is a data science researcher, developer, and educator with more than twenty-eight years of data science practice in software development, property and casualty insurance, and retail financial service industries. Dr. Lam has strong expertise in developing statistical and machine learning algorithms, customizing solutions for analyzing person data, and implementing solutions as in major analytical software.
Dr. Lam is currently a Principal Research Statistician Developer at the SAS Institute, and on the faculty of the Department of Computer Science at the Illinois Institute of Technology and the Master of Science in Analytics program at the University of Chicago. Before joining the SAS Institute, Dr. Lam had a rewarding career in directing the development of analytical features in SPSS. In between these two major statistical software vendors, Allstate recruited Dr. Lam to sophisticate their insurance rating plans and to discover their most valuable customers. Dr. Lam also joined Chase to develop new performance assessment models for recruiting retail financial staff and for increasing their tenures at the bank.
Dr. Lam has co-published the book Using Data Analysis to Improve Student Learning: Toward 100% Proficiency. Dr. Lam earned his bachelor’s degree in mathematics and his master’s degree in statistics from the Chinese University of Hong Kong. Dr. Lam also earned his doctorate in statistics from the University of Chicago.