Machine Learning

Machine Learning

A Comprehensive Beginner's Guide

B R, Akshay; Murugesh, T S; Pulari, Sini Raj; Vasudevan, Shriram K

Taylor & Francis Ltd

07/2024

248

Dura

9781032676654

15 a 20 dias

Descrição não disponível.
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.
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machine learning;artificial intelligence;projects;analysis;classification