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 ...
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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
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Add this copy of Data Science Revealed: With Feature Engineering, Data to cart. $28.01, good condition, Sold by Big River Books rated 5.0 out of 5 stars, ships from Powder Springs, GA, UNITED STATES, published 2021 by Apress.
Add this copy of Data Science Revealed: With Feature Engineering, Data to cart. $37.50, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2021 by Apress.
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New. Trade paperback (US). Glued binding. 252 p. Contains: Unspecified, Illustrations, black & white. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Data Science Revealed: With Feature Engineering, Data to cart. $37.51, new condition, Sold by BargainBookStores rated 3.0 out of 5 stars, ships from Grand Rapids, MI, UNITED STATES, published 2021 by Apress.
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New in New jacket. Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning (Paperback or Softback)
