In the practical portion of Week 1, participants worked directly in Google Colab to explore and analyze data, create visualizations, and build their first machine learning workflow. Using pandas and NumPy, they performed data cleaning and exploratory analysis, then implemented a basic classification model with SciKit-learn, evaluated its performance using accuracy scores and confusion matrices, and interpreted results step-by-step. Along the way, we reinforced best practices for working in notebook environments and demonstrated how Google’s ML ecosystem supports development, from experimenting with models in Colab to understanding how TensorFlow fits into more advanced stages of the pipeline. This hands-on session helped participants move from theory to practice and feel comfortable executing their first end-to-end ML experiment.