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Class imbalance is one of the most common and tricky problems in real-world classification tasks.

This 90-minute session introduces participants to the critical topic of model interpretability, focusing on feature importance using SHAP (Shapley Additive explanations).

In this 90-minute hands-on session, you’ll dive deep into creating, manipulating, and performing operations with tensors: the essential data structures behind every modern machine learning model.

Step into the world of deep learning by building your first Artificial Neural Network! In this 90-minute, beginner-friendly session,

This session will introduce you to Convolutional Neural Networks (CNNs): the deep learning architecture that powers technologies like facial recognition, autonomous vehicles, and medical image analysis.

In this 90-minute hands-on session, you’ll learn essential text preprocessing steps like tokenization, padding, and vocabulary building, and see how to turn words into numbers using embedding layers.

In this 90-minute session, you’ll learn the critical hyperparameters that control how your model learns, including optimizers, loss functions, evaluation metrics, and callbacks.

Deep learning models are data-hungry; but what if you could leverage a pre-trained network to solve your problem with much less data?

In deep learning, a model can either underfit (not learn enough from the data) or overfit (memorize the data, losing the ability to generalize).