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Through 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.