000			03417nam a22002777a 4500
005			20251102144403.0
008			251102b |||||||| |||| 00| 0 eng d
020			_a9781032676661
082			_a006.31 _bAKS
100			_aB R, Akshay _925993
245			_aMachine learning: _ba comprehensive beginner's guide
260			_aBoca Raton _bRoutledge _c2025
300			_ax, 248 p.
365			_aGBP _b47.99
500			_aTable 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]
520			_aMachine 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)
650			_aData--Mining
650			_aMachine learning
650			_aDataset--Networks _925994
650			_aArtificial Intelligence
700			_aPulari, Sini Raj _925995
700			_aMurugesh, T.S. _925996
700			_aShriram K _925997
700			_aVasudevan _925998
942			_cBK _2ddc
999			_c10410 _d10410