Tshepo Chris Nokeri - Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6869-8 . For more detailed information, please visit www.apress.com/source-code .

Welcome to Data Science Revealed. This book is your guide to solving practical and real-world problems using data science procedures. It gives insight into data science techniques, such as data engineering and visualization, statistical modeling, machine learning, and deep learning. It has a rich set of examples on how to select variables, optimize hyperparameters, develop pipelines, and train, test and validate machine and deep learning models. Each chapter contains a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model.

First, it conceals the parametric method or linear model and the means for combating underfitting or overfitting using regularization techniques such as lasso and ridge. Next, it concludes complex regression by presenting time-series smoothening, decomposition, and forecasting. Then, it takes a fresh look at a nonparametric model for binary classification, known as logistic regression, and ensemble methods such as decision tree, support vector machine, and nave Bayes. Next, it covers the most popular nonparametric method for time-event data, recognized as the Kaplan-Meier estimator. It also covers ways of solving a classification problem using artificial neural networks, like the restricted Boltzmann machine, multilayer perceptron, and deep belief network. Then, it summarizes unsupervised learning by uncovering clustering techniques, such as K-means, agglomerative and DBSCAN, and dimension reduction techniques such as feature importance, principal component analysis, and linear discriminant analysis. In addition, it introduces driverless artificial intelligence using H2O.

This book targets beginner to intermediate data scientists and machine learning engineers who want to learn the full data science process. Before exploring the contents of this book, ensure that you understand the basics of statistics, Python programming, and probability theories. Also, youll need to install the packages mentioned in the previous list in your environment.

This is my first book, which makes it significant to me. Writing a single-authored book is demanding, but I received firm support and active encouragement from my family and dear friends. Many heartfelt thanks to Professor Chris William Callaghan and Mrs. Renette Krommenhoek from the University of the Witwatersrand. They gallantly helped spark my considerable interest in combating practical and real-world problems using advanced analytics. I would not have completed this book without the valuable help of the dedicated publishing team at Apress, which compromises Aditee Mirashi and Celestin Suresh John. They trusted and ushered me throughout the writing and editing process. Last, humble thanks to all of you reading this; I earnestly hope you find it helpful.