Learn 3D Image Classification with Python and Keras

Learn to predict viral pneumonia in CT scans with the help of 3D CNNs in Python and Keras : Hands-on

Description

Welcome to the “Learn 3D Image Classification with Python and Keras” course. In this comprehensive and hands-on course, you will learn how to build a powerful 3D convolutional neural network (CNN) for classifying CT scans. With the use of the Google Colab platform, Python, and Keras in TensorFlow, you will be able to effectively analyse medical images and predict the presence of viral pneumonia in computer tomography (CT) scans.

Medical imaging plays a vital role in disease diagnosis, and this course will provide you with the necessary skills and techniques to excel in this field. You will be able to tackle real-world challenges and gain a strong foundation in 3D image classification and deep learning. This is an excellent opportunity for healthcare professionals, data scientists, and anyone looking to advance their AI skills.

By the end of this course, you will have a complete understanding of how to classify 3D images using Python and Keras. You will have a portfolio project that you can showcase to potential employers and be able to confidently apply your skills in a professional setting. With its clear and concise approach, this course is designed to maximize your learning potential in the shortest time possible.

Enroll now and take the first step towards a fulfilling career in 3D image classification and AI. Happy Learning!

Who this course is for:

• Anyone who is interested in learning about 3D image classification and building a 3D convolutional neural network using Python, Keras, and TensorFlow on the Google Colab platform.
• AI enthusiasts who are eager to learn how to develop a deep learning model from scratch and want to apply their knowledge to the medical imaging domain.
• Data scientists and machine learning engineers who are interested in expanding their skill set in the field of medical imaging analysis and want to work on real-world projects.
• Healthcare professionals, such as radiologists and medical technicians, who are interested in utilizing advanced AI techniques to improve the accuracy of disease diagnosis from medical imaging data.