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Machine learning : a comprehensive beginner's guide / Akshay B R, Sini Raj Pulari, T.S. Murugesh, Shriram K. Vasudevan.

By: Contributor(s): Material type: TextTextPublisher: Boca Raton, FL : CRC Press, 2025Edition: First editionDescription: 248 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781032676661
Subject(s): DDC classification:
  • 23 006.31 A.B.M
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: 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.
Summary: 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.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books Media and mass communication Library C1 006.31 R.A.M C.1 Available MA0002537
Total holds: 0

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.

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