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.
| ISBN: | 9789819727223 |
| Publication date: | 10th May 2025 |
| Author: | K C Santosh, Rodrigue Rizk, Siddhi K Bajracharya |
| Publisher: | Springer an imprint of Springer Nature Singapore |
| Format: | Paperback |
| Pagination: | 127 pages |
| Series: | Studies in Computational Intelligence |
| Genres: |
Artificial intelligence Machine learning Databases |
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.
Cracking the Machine Learning Code features in the following genres: Artificial intelligence, Machine learning, Databases
Cracking the Machine Learning Code is available in Paperback, Hardback
Cracking the Machine Learning Code was written by K C Santosh, Rodrigue Rizk, Siddhi K Bajracharya and published by Springer an imprint of Springer Nature Singapore
Cracking the Machine Learning Code has 127 pages
Yes it is part of Studies in Computational Intelligence series
£125.99