This foundational course provides hardware engineers, FPGA designers, and embedded software engineers with the machine learning and deep learning knowledge required to participate effectively in AI hardware acceleration projects. Starting from first principles of supervised learning and progressing through convolutional neural networks, Transformer architectures, quantization mathematics, and the end to end model development and deployment lifecycle, participants build the vocabulary and intuition needed to engage in hardware focused AI work. The presentation material is complimented with hands on lab exercises. Concepts are reinforced through examples such as; parameter counts, FLOP budgets, memory bandwidth demands, and precision tradeoffs so that students leave ready to engage directly with Altera AI Suite and HLS based accelerator development.