Machine learning : a comprehensive beginner's guide /
R, Akshay B.,
Machine learning : a comprehensive beginner's guide / Akshay B R, Sini Raj Pulari, T.S. Murugesh, Shriram K. Vasudevan. - First edition. - 248 pages : illustrations ; 24 cm.
Includes index.
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: 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. EDA and classification on wine quality dataset with oneAPI -- 15. Cats vs. Dogs classification using deep learning models optimized with oneAPI -- 16. Placement predictions with outlier removal -- 17. Mushroom classification with oneAPI -- 18. Kidney disease prediction with oneAPI -- 19. Multiclass flower classification with ResNet and VGG16 using oneAPI -- 20. Twitter sentiment analysis with oneAPI and NLP.
A practical beginner's guide to machine learning using real-world datasets and modern tools like oneAPI, featuring 20 hands-on projects covering classification, prediction, NLP, deep learning, and performance evaluation techniques.
9781032676661
Machine learning.
Artificial intelligence--Data processing.
Algorithms--Data analysis.
Deep learning (Machine learning).
Data mining.
006.31 / A.B.M
Machine learning : a comprehensive beginner's guide / Akshay B R, Sini Raj Pulari, T.S. Murugesh, Shriram K. Vasudevan. - First edition. - 248 pages : illustrations ; 24 cm.
Includes index.
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: 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. EDA and classification on wine quality dataset with oneAPI -- 15. Cats vs. Dogs classification using deep learning models optimized with oneAPI -- 16. Placement predictions with outlier removal -- 17. Mushroom classification with oneAPI -- 18. Kidney disease prediction with oneAPI -- 19. Multiclass flower classification with ResNet and VGG16 using oneAPI -- 20. Twitter sentiment analysis with oneAPI and NLP.
A practical beginner's guide to machine learning using real-world datasets and modern tools like oneAPI, featuring 20 hands-on projects covering classification, prediction, NLP, deep learning, and performance evaluation techniques.
9781032676661
Machine learning.
Artificial intelligence--Data processing.
Algorithms--Data analysis.
Deep learning (Machine learning).
Data mining.
006.31 / A.B.M