| Title | : | Cracking the Machine Learning Code: Technicality or Innovation? |
|---|---|---|
| Author | : | K.C. Santosh, Rodrigue Rizk & Siddhi K. Bajracharya |
| Release | : | 2024-05-08 |
| Kind | : | ebook |
| Genre | : | Computers & Internet, Books, Professional & Technical, Engineering, Science & Nature, Mathematics, Databases |
| Size | : | 33079113 |
| Employing off-the-shelf machine learning models is not an innovation. The journey through technicalities and innovation in the machine learning field is ongoing, and we hope this book serves as a compass, guiding the readers through the evolving landscape of artificial intelligence. It typically includes model selection, parameter tuning and optimization, use of pre-trained models and transfer learning, right use of limited data, model interpretability and explainability, feature engineering and autoML robustness and security, and computational cost – efficiency and scalability. Innovation in building machine learning models involves a continuous cycle of exploration, experimentation, and improvement, with a focus on pushing the boundaries of what is achievable while considering ethical implications and real-world applicability. The book is aimed at providing a clear guidance that one should not be limited to building pre-trained models to solve problems using the off-the-self basic building blocks. With primarily three different data types: numerical, textual, and image data, we offer practical applications such as predictive analysis for finance and housing, text mining from media/news, and abnormality screening for medical imaging informatics. To facilitate comprehension and reproducibility, authors offer GitHub source code encompassing fundamental components and advanced machine learning tools. |
