Overview
- Covers the parametric, ensemble, and the non-parametric methods
- Presents techniques to improve model performance in pre- and post-training
- Summarizes H2O driverless AI and automatic forecasting using Prophet
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Table of contents (13 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model.
The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O.
After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data.
What You Will Learn
- Design, develop, train, and validate machine learning and deep learning models
- Find optimal hyper parameters for superior model performance
- Improve model performance using techniques such as dimension reduction and regularization
- Extract meaningful insights for decision making using data visualization
Who This Book Is For
Beginning and intermediate level data scientists and machine learning engineers
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Data Science Revealed
Book Subtitle: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning
Authors: Tshepo Chris Nokeri
DOI: https://doi.org/10.1007/978-1-4842-6870-4
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Tshepo Chris Nokeri 2021
Softcover ISBN: 978-1-4842-6869-8Published: 07 March 2021
eBook ISBN: 978-1-4842-6870-4Published: 06 March 2021
Edition Number: 1
Number of Pages: XX, 252
Number of Illustrations: 95 b/w illustrations
Topics: Machine Learning, Python