This course provides comprehensive hands-on training in building language models from scratch, covering tokenization, transformer architecture, training, optimization, and deployment. Students complete 5 intensive assignments implementing components like tokenizers, attention mechanisms, distributed training, data processing, and alignment techniques. Prerequisites include Python proficiency, deep learning experience, and mathematical foundations. The 5-unit implementation-heavy course follows an operating systems approach, guiding students through the entire language model development process including data collection, model construction, training, and evaluation.