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Applied Analytics through Case Studies Using SAS and R

predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business;

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Supervised Machine Learning

interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework;

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Supervised Machine Learning

interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework;

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Learning SAS by Example

Learning SAS® by Example: A Programmer’s Guide, Second Edition, teaches SAS programming from very basic concepts to more advanced topics;

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Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition

data, and incorporating textual records into your predictive model with SAS Text Miner. Finally, part 4 takes segmentation to a new level with;

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MATLAB for Machine Learning

and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This;

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Learning SAS by Example

Learning SAS® by Example, A Programmer’s Guide, Second Edition, teaches SAS programming from very basic concepts to more advanced topics;

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Machine Learning with SAP

Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;

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The Little Sas Book for Enterprise Guide 4.1

Learning to use SAS Enterprise Guide has never been easier. With this handbook, Slaugher and Delwiche introduce the SAS Enterprise Guide;

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Static Analysis

This book constitutes the refereed proceedings of the 26th International Symposium on Static Analysis, SAS 2019, held in Porto, Portugal;

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Practical Machine Learning with Python

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world;

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AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

Prepare to achieve AWS Machine Learning Specialty certification with this complete, up-to-date guide and take the exam with confidence;

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Practical Machine Learning with Rust

"Explore machine learning in Rust and learn about the intricacies of creating machine learning applications"--;

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Carpenter's Complete Guide to the SAS Macro Language, Third Edition

his successful second edition of Carpenter's Complete Guide to the SAS Macro Language with an extensive collection of new macro language;

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Machine Learning with scikit-learn Quick Start Guide

learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

skills and knowledge needed to prepare data for analysis with Azure Machine Learning; find key variables describing your data's behavior; develop;

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Machine Learning with R

Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine;

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Python

deep learning algorithms * Increase predictive accuracy with deep learning and scalable data-handling techniques * Work with modern state;

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Introduction to Machine Learning with Python

machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn;

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Practical Automated Machine Learning on Azure Using Azure Machine Learning to Quickly Build AI Solutions

Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you'll learn how to;

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SAS Essentials

introduce users to statistical analysis using SAS Procedures. Featuring self-contained chapters to enhance the learning process, the Second Edition;

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Machine Learning Using R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;

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Einde inhoud

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