Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning) - Image 1

£74.42

Springer Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Name: Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)
Brand: Springer
Price: 74.42 GBP
Availability: InStock

AI & Machine Learning

This is the usual price. Wait for it to drop, or tell us your number.

£74 today · usual range £0–£0 · best ever £64

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 537 days • 537 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 537 days • 5 price ranges

Days at Price

Current Price

Price Analysis

Most common range: £76-80 (277 days, 51.6%)

Price range: £64 - £84

Price levels: 5 price ranges over 537 days

Description

This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3031174828
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 17 February 2023
Listed Since: 20 August 2022

Barcode

No barcode data available

Springer Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

This is the usual price. Wait for it to drop, or tell us your number.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems: 1

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems: 1

Big Data and Social Science: Data Science Methods and Tools for Research and Practice (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Machine Learning Foundations: Volume 1: Supervised Learning

The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics

Lecture Notes in Computational Intelligence and Decision Making: 2021 International Scientific Conference "Intellectual Systems of Decision-making and ... and Communications Technologies, 77)

No-Code Data Science: Mastering Advanced Analytics, Machine Learning, and Artificial Intelligence

A Practical Machine Learning with R: Tutorials and Case Studies

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models

Proceedings of the 22nd Engineering Applications of Neural Networks Conference: EANN 2021: 3 (Proceedings of the International Neural Networks Society, 3)

Advances in Computer Science for Engineering and Education III: 1247 (Advances in Intelligent Systems and Computing, 1247)

Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019): 896 (Studies in Computational Intelligence, 896)

Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition (360 Degree Business)

Practical Issues of Intelligent Innovations: 140 (Studies in Systems, Decision and Control, 140)

Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers

Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI, Volume 2: 956 (Studies in Computational Intelligence, 956)

Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, ... (Lecture Notes in Computer Science, 12976)

Machine Learning and Systems Engineering: 68 (Lecture Notes in Electrical Engineering, 68)

Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach