AI and Deep Learning Made Easy for Medical Imaging

Your First Step into ‘AI for Healthcare’ for Building Smarter Imaging Models from Scratch.

What you’ll learn
Build a Strong Foundation in AI, ML, CNN and DL concepts through simple real-world examples.
Design basic neural networks using neurons, weights, bias, and activation functions and visualize how data flows through the network.
Build and train a simple DL models using frameworks like PyTorch and experiment with learning rate, batch size, epochs, and optimize the model.
Evaluate models using accuracy and loss metrics, identify overfitting or underfitting, and apply techniques like data augmentation and regularization.

Requirements
Familiarity with Python (Recommended) – A basic understanding of Python syntax (variables, loops, functions, class) will make coding exercises easier to follow.
Mathematical Foundations (Optional but Helpful) – Basic knowledge of high-school mathematics, especially algebra and simple statistics, will help in understanding concepts like activation functions, weights, bias, and loss functions.

Description
Artificial Intelligence and Deep Learning are transforming the field of medical imaging and helping doctors detect diseases faster, more accurately, and at scale.“AI and Deep Learning Made Easy for Medical Imaging” is a beginner-friendly, hands-on course designed to help you understand and apply the core concepts behind this revolution.In this course, you’ll start with the fundamentals of AI and Machine Learning, move into the structure of Artificial Neural Networks (ANNs), and then explore Convolutional Neural Networks (CNNs) — the backbone of image analysis. Step by step, you’ll learn how to build and train deep learning models that can classify and detect patterns in medical images like X-rays. Through a blend of interactive tutorials, quizzes, and assignments, you’ll gain practical experience in:Understanding neurons, weights, bias, and activation functionsBuilding and training deep learning models using PythonApplying CNNs for real-world medical image classification tasksBy the end of this course, you’ll have the confidence to design and implement your own deep learning models for medical imaging.  Take your first step into the world of AI-driven healthcare innovation.Fundamental python programming will help you benefit to build the CNN model. But no prior AI experience is required, just your curiosity, persistence, and a passion for learning will make difference in your learning journey.

Who this course is for
Students and fresh graduates from engineering, computer science, or life science backgrounds who want to enter the AI field.
Professionals from non-AI domains looking to transition into data science, AI, or machine learning roles.
Healthcare, biology, and imaging enthusiasts interested in understanding how AI models learn, make predictions, and can be applied in real-world scenarios.
Anyone curious about AI who wants to move from theory to hands-on implementation — by learning how to build and train simple neural networks and CNNs from scratch.

https://anonymz.com/?https://www.udemy.com/course/ai-and-deep-learning-made-easy-for-medical-imaging/