TY  - BOOK
AU  - B R, Akshay
AU  - Pulari, Sini Raj
AU  - Murugesh, T.S.
AU  - Shriram K
AU  - Vasudevan
TI  - Machine learning: a comprehensive beginner's guide
SN  - 9781032676661
U1  - 006.31 
PY  - 2025///
CY  - Boca Raton
PB  - Routledge
KW  - Data--Mining
KW  - Machine learning
KW  - Dataset--Networks
KW  - Artificial Intelligence
N1  - Table of contents:
Introduction: What is Machine Learning?

1. Exploring the Iris dataset

2. Heart failure prediction with oneAPI

3. Handling water quality dataset

4. Breast cancer classification with hybrid ML models

5. Flower recognition with Kaggle dataset and Gradio interface

6. Drug classification with hyperparameter tuning

7. Evaluating model performance: Metrics for diabetes prediction

8. Parkinson’s disease detection: An overview with feature engineering and outlier analysis

9. Sonar mines vs. rock prediction using ensemble learning

10. Bankruptcy risk prediction

11. Hotel reservation prediction

12. Crop recommendation prediction

13. Brain tumor classification

14. Exploratory data analysis and classification on wine quality dataset with oneAPI

15. Cats vs. Dogs classification using deep learning models optimized with oneAPI

16. Maximizing placement predictions with outlier removal

17. A deep dive into Mushroom classification with oneAPI

18. Smart healthcare – Machine learning approaches for kidney disease prediction with oneAPI

19. A deep dive into multiclass flower classification with ResNet and VGG16 using oneAPI

20. Dive into X (formerly Twitter’s) emotions using oneAPI – Sentiment analysis with NLP

[https://www.routledge.com/Machine-Learning-A-Comprehensive-Beginners-Guide/BR-Pulari-Murugesh-Vasudevan/p/book/9781032676661]
N2  - Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.

(https://www.routledge.com/Machine-Learning-A-Comprehensive-Beginners-Guide/BR-Pulari-Murugesh-Vasudevan/p/book/9781032676661)
ER  -