Course Outcomes

Conceptual Understanding

• Differentiate between machine learning, deep learning, and traditional statistical models

• Understand the basics of Alzheimer’s disease, from affected brain regions to disease progression symptoms

• Analyze the limitations of conventional diagnostic methods and identify how AI improves early detection

Technical Skills

• Build and train a Convolutional Neural Network to classify brain MRI images as Alzheimer’s or healthy

• Prepare and preprocess medical imaging data for deep learning model input

• Work with core CNN architecture elements including layered feature extraction and classification

Assess model performance using standard evaluation metrics like accuracy and precision

Applied Learning

• Develop an end-to-end image classification pipeline using convolutional neural networks on MRI brain scans

Translate real brain imaging data into diagnostic predictions through structured coding exercises and model evaluation

• Complete a guided mini-project simulating a real-world diagnostic AI workflow, from preprocessing to model interpretation

Week 1

Foundations of AI and Alzheimer’s Disease

Introduction to machine learning, deep learning, and CNNs; overview of Alzheimer’s pathology, affected brain regions, and diagnostic challenges.

Week 2

 
         Model Implementatin and Training Step-by-step development of a CNN model using Python and Keras, along with image preprocessing and initial model training. 
        

Week 3

Model Evaluation and Performance Analysis

Analyze classification results using precision, recall, accuracy, and F1-score; interpret errors and model behavior.

Week 4

Final Project and Ethical Implications

Execute a mini-project involving real MRI data, evaluate outcomes, and explore ethical considerations in deploying AI in clinical workflows.

##M.Sc Biotechnoly (Vellore Institute of Technology)##

Using Convolutional Neural Network for Alzheimer's prediction
4-Week Online Project-Based Certificate Course

Course Outcomes

Conceptual Understanding

Technical Skills

Applied Learning

Foundations of AI and Alzheimer’s Disease

Introduction to machine learning, deep learning, and CNNs; overview of Alzheimer’s pathology, affected brain regions, and diagnostic challenges.

Model Evaluation and Performance Analysis

Analyze classification results using precision, recall, accuracy, and F1-score; interpret errors and model behavior.

Final Project and Ethical Implications

Execute a mini-project involving real MRI data, evaluate outcomes, and explore ethical considerations in deploying AI in clinical workflows.

{{Bhavna Rao}}

ENROLL HERE

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Using Convolutional Neural Network for Alzheimer's prediction4-Week Online Project-Based Certificate Course

Course Outcomes

Conceptual Understanding

Technical Skills

Applied Learning

Foundations of AI and Alzheimer’s Disease Introduction to machine learning, deep learning, and CNNs; overview of Alzheimer’s pathology, affected brain regions, and diagnostic challenges.

Model Evaluation and Performance Analysis Analyze classification results using precision, recall, accuracy, and F1-score; interpret errors and model behavior.

Final Project and Ethical Implications Execute a mini-project involving real MRI data, evaluate outcomes, and explore ethical considerations in deploying AI in clinical workflows.

{{Bhavna Rao}}

ENROLL HERE

Using Convolutional Neural Network for Alzheimer's prediction
4-Week Online Project-Based Certificate Course

Foundations of AI and Alzheimer’s Disease

Introduction to machine learning, deep learning, and CNNs; overview of Alzheimer’s pathology, affected brain regions, and diagnostic challenges.

Model Evaluation and Performance Analysis

Analyze classification results using precision, recall, accuracy, and F1-score; interpret errors and model behavior.

Final Project and Ethical Implications

Execute a mini-project involving real MRI data, evaluate outcomes, and explore ethical considerations in deploying AI in clinical workflows.