Cracking the Machine Learning Code: Technicality or Innovation?

Name: Cracking the Machine Learning Code: Technicality or Innovation?
Brand: Springer
Availability: OutOfStock
Rating: 5 (1 reviews)

By Kc Santosh

0 - Default Title

Description

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.

Product details

Binding:

Paperback

Number of Pages:

148

Release Date:

2025-05-10

Publication Date:

2025-05-10

Publisher:

Springer

Languages:

Original: English

ISBN10:

9819727227

ISBN13:

9789819727223

GPSR Manufacturer Reference:

[email protected]

Weight:

236 g

Height:

155 cm

Width:

235 cm

Thickness:

9 cm

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