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Course Description

'Machine Learning Foundations' is an advanced course built on a prerequisite knowledge base acquired in 'Python for Machine Learning.' It's a deep dive into convolutional neural networks (CNNs) and their intricate workings. Beginning with a mathematical exploration of convolutions, the course elucidates how these operations extract crucial features from data, laying a strong theoretical groundwork.

The curriculum progresses to tackle nuanced concepts like working with activations to isolate discriminative data points and determining the number of parameters within machine learning models. Students learn the profound effects of stride, padding, kernel size, and count on feature extraction, enabling them to make informed choices when designing models.

An integral part of the course is its hands-on approach. Students delve into designing layers incorporating Convolutional Neural Networks, Batch Normalization, and Recti-Linear Unit activation (CNN, BN, ReLU). They also learn to construct the ResNet architecture from scratch, including Bottleneck Layer bundles. Additionally, the course addresses practical aspects such as data loading, preparation, and augmentation using third-party libraries, providing a holistic understanding of applying these concepts in real-world scenarios. This comprehensive blend of theory and practical application equips students with the expertise to comprehend, construct, and optimize sophisticated machine learning models centered around convolutional neural networks.