Practical AI for Healthcare Professionals:Machine Learning with Numpy, Scikit-learn, and TensorFlow
Publisher Finelybook 出版社：Apress; 1st ed. edition (December 14, 2021)
pages 页数：268 pages
Use Artificial Intelligence (AI) to analyze and diagnose what previously could only be handled Author:trained medical professionals. This book gives an introduction to practical AI, focusing on real-life medical problems, how to solve them with actual code, and how to evaluate the efficacy of these solutions.
You’ll start Author:learning how to diagnose problems as ones that can and cannot be solved with AI or computer science algorithms. If you’re not familiar with those algorithms, that’s not a problem. You’ll learn the basics of algorithms and neural networks and when each should be applied. Then you’ll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The TensorFlow library alogn with Numpy and Scikit-Learn are covered, too.
Once you’ve mastered those basic computer science concepts, you can dive into three projects with code, implementation details and explanation, and diagnostic utility analysis. These projects give you the change to explore using machine learning algorithms for diagnosing diabetes from patient data, using basic neural networks for heart disease prediction from cardiac data, and using convolutional networks for brain tumor segmentation from MRI scans
The topics and projects covered not only encompass areas of the medical field where AI is already playing a major role but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved Author:AI, and how to program solutions to problems using modern libraries, such as TensorFlow. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients.
What You’ll Learn
Distinguish between problems that currently can and cannot be solved with AI
Master programming concepts not familiar to physicians, such as libraries, coding, and creating and training ML models
Perform dataset analysis with decision trees, SVMs, and neural networks.