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Application of Python in the Healthcare Technology: Medical Data Analysis and Developing ML/AI Models

Autor Praveen Kumar M
en Limba Engleză Paperback – dec 2025
Application of Python in Healthcare Technology: Medical Data Analysis and Developing ML/AI Models provides comprehensive coverage of the Python programming language from a medical-based perspective. With a strong focus on utility-based learning rather than technical-based learning of Python, this title clarifies how the application of Python in the healthcare technology industry has a meaningful impact on patient care through the development of deep learning models for early prediction of diseases and the development of Clinical Decision Support System (CDS). In 13 chapters, an extensive range of topics pertaining to the intersection of Python and medical sciences is covered.

Coverage defines how to set up an environment to work with python, describes Python class, methods, and functions, explains both basic and advanced statistical analysis in Python, and provides a tool to assist in the development of deep learning models such as API, Git, Github, NumPy, Pandas, and SciPy.


  • Covers Python programming, from beginner level to advanced
  • Uses the programming language Python to show doctors and other allied healthcare graduates how to impact patient care with world-class technology
  • Follows the principle of utility-based learning rather than technical-based learning to suit the needs of medical graduate audience
  • Covers hot topics like deep learning and advanced statistics
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Specificații

ISBN-13: 9780443219191
ISBN-10: 0443219192
Pagini: 180
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

1. Requirements to be installed for setting up an environment to work with python
2. Built-in Data structures in python
3. Python Class, Methods and Functions
4. Analyzing data using Pandas and numpy
5. Basic statistical analysis in Python
6. Advanced statistical analysis in Python
7. Graphical visualization using matplotlib
8. Developing machine learning models using scikit-learn
9. Developing deep learning models using tensorflow
10. Developing image classifier and image identifier model using deep learning techniques
11. Accessing web using Active Programming Interface (API)
12. Version control system using Git and Github
13. Websites for staying abreast with the latest developments in python