📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Deep Learning Fundamentals, from foundations to a certified capstone project.
Networks
• Perceptrons, activation functions and why non-linearity is essential
• Forward pass, loss functions and backpropagation mechanics
• Gradient descent variants and the role of the learning rate
Training
• Initialisation, normalisation and vanishing or exploding gradients
• Regularisation: dropout, weight decay and early stopping
• Reading loss curves to diagnose what is going wrong
Vision
• Convolution, pooling and receptive fields
• Standard architectures and residual connections
• Data augmentation and transfer learning from pretrained backbones
Sequences
• Sequence modelling with RNNs and LSTMs, and their limitations
• Attention and the transformer block
• Tokenisation and embeddings for text
Practice
• PyTorch training loops, datasets and dataloaders
• GPU memory, batch size and mixed precision
• Overfitting a single batch as a first debugging step
Earn government-registered certification in Deep Learning Fundamentals
e-Certificate and e-Marksheet issued on successful completion.
