Cantitate/Preț
Produs

Statistical Modeling in Machine Learning: Concepts and Applications

Editat de Tilottama Goswami, G. R. Sinha
en Limba Engleză Paperback – 6 noi 2022
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning.
Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.


  • Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials
  • Presents a step-by-step approach from fundamentals to advanced techniques
  • Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples
Citește tot Restrânge

Preț: 126748 lei

Preț vechi: 158435 lei
-20% Nou

Puncte Express: 1901

Preț estimativ în valută:
24257 25197$ 20149£

Carte tipărită la comandă

Livrare economică 03-17 februarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780323917766
ISBN-10: 0323917763
Pagini: 396
Dimensiuni: 191 x 235 mm
Greutate: 0.68 kg
Editura: ELSEVIER SCIENCE

Public țintă

Researchers, developers, and industry professionals in Information Technology and Computer Science, such as developers of AI, Machine Learning,and Deep Learning, as well as other research fields, including Biomedical.

Cuprins

1. Introduction to Statistical Modelling in Machine Learning - A Case Study
2. A Technique of Data Collection- Web Scraping with Python
3. Analysis of Covid-19 using Machine Learning Techniques
4. Discriminative Dictionary Learning based on Statistical Methods
5. Artificial Intelligence based Uncertainty Quantification technique for External flow CFD simulations
6. Music Genres Classification
7. Classification Model of Machine Learning for Medical Data Analysis
8. Regression Models for Machine learning
9. Model Selection and Regularization
10. Data Clustering using Unsupervised Machine Learning
11. Emotion-based classification through fuzzy entropy enhanced FCM clustering
12. Fundamental Optimization Methods for Machine Learning
13. Stochastic Optimization of Industrial Grinding Operation through Data-Driven Robust Optimization
14. Dimensionality Reduction using PCAs in Feature Partitioning Framework
15. Impact of Mid-Day Meal Scheme in Primary Schools in India using Exploratory Data Analysis and Data Visualisation
16. Nonlinear System Identification of Environmental pollutants using Recurrent Neural Networks and Global Sensitivity Analysis
17. Comparative Study of Automated Deep Learning Techniques for Wind Time Series Forecasting