Santosh K. Cracking the Machine Learning Code. Technicality or Innovation 2024
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Santosh K. Cracking the Machine Learning Code. Technicality or Innovation 2024
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Category: Other
Total size: 5.75 MB
Added: 2025-03-10 23:38:55
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Info Hash: 9D64DEC632C51C2EE4B8AADE1F8E071155CCEA1D
Last updated: 4.7 hours ago
Description:
Textbook in PDF format
Covers three primary data types: numerical, textual, and image data
Offers GitHub source code encompassing fundamental components and advanced machine learning tools
Serves as a reference for researchers, students, practitioners, and policymakers
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