Ramchandra S Mangrulkar – författare

Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background.The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models.After reading the book, you will be able to put predictive analytics principles into practice.What You Will LearnUnderstand modeling, estimating, and evaluating models for forecastingImplement Partial F-Test and Variable Selection MethodDemonstrate each analysis model in R Studio and SASUnderstand SLR and MLR Analysis modelsWho This Book Is ForStudents and professionals in the field of data analysis and intelligence applications

Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background.

The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models.

After reading the book, you will be able to put predictive analytics principles into practice.

What You Will Learn

Understand modeling, estimating, and evaluating models for forecastingImplement Partial F-Test and Variable Selection MethodDemonstrate each analysis model in R Studio and SASUnderstand SLR and MLR Analysis models

Who This Book Is For

Students and professionals in the field of data analysis and intelligence applications

Explore the convergence of deep learning and GPU technology. This book is a complete guide for those wishing to use GPUs to accelerate AI workflows.The book is meant to make complex concepts understandable, with step-by-step instructions on how to set up and use GPUs in deep learning applications. Starting with an introduction to the fundamentals, you'll dive into progressive topics like Convolutional Neural Networks (CNNs) and sequence models, exploring how GPU optimization boosts performance. Further, you will learn the power of generative models, and take your skills by deploying AI models on edge devices. Finally, you will master the art of scaling and distributed training to handle large datasets and complex tasks efficiently.This book is your roadmap to becoming proficient in deep learning and harnessing the full potential of GPUs.What You Will Learn:How to apply deep learning techniques on GPUs to solve challenging AI problems.Optimizing neural networks for faster training and inference on GPUsIntegration of GPUs with Microsoft CopilotsImplementing VAEs (Variational Autoencoders) with TensorFlow and PyTorchWho This Book Is For:Industry IT professionals in AI. Students pursuing undergraduate and postgraduate degrees in Engineering, Computer Science, Data Science.