Machine Learning: Introducing Available Datasets with Python
Par šo e-grāmatu
tion and analysis of publicly available benchmark datasets. It presents a systematic treat
ment of data acquisition, inspection, preprocessing, and evaluation within supervised learning
frameworks.
Key topics include statistical characterization of datasets, data normalization, categorical
encoding, handling class imbalance, feature selection and extraction, dimensionality reduction,
and model evaluation metrics. Computational examples are provided using Python, with
implementations based on scikit-learn and PyTorch.
The text is intended for students and practitioners seeking a rigorous and practical foun
dation in data management for machine learning, with applications to both classification and
regression problems.
Citas grāmatas
Par autoru
Dr. G.R. (Gui-Rong) Liu is an expert in computational mechanics and numerical methods, currently serving as a Professor of Aerospace Engineering and Engineering Mechanics at the University of Cincinnati. A prolific researcher and educator, Dr. Liu is recognized for pioneering meshfree methods, smoothed finite element methods (S-FEM), and smoothed particle hydrodynamics (SPH). These innovations have significantly advanced simulation techniques in both solid and fluid mechanics.
In recent years, Dr. Liu has authored several textbooks and reference works for courses and research in areas such as artificial intelligence, machine learning, mathematics, computational methods, mechanics of materials, solid mechanics, engineering mechanics, applied mechanics, and fluid dynamics.
