[Verse 1] Start with your environment, make it clean and bright Virtual env or conda, keep dependencies right NumPy for the numbers, Pandas for the frames Matplotlib and Seaborn paint your data games [Chorus] Build your toolbox strong and true Python ecosystem made for you Jupyter notebooks, experiment and explore Git and DVC, version control and more Set it up reproducible, that's the way to go Python AI tooling, everything you need to know [Verse 2] Docker containers wrap it up, consistent every time Jupyter Lab or Google Colab, coding so sublime VS Code extensions help you write with AI might Import all your libraries, data science takes flight [Chorus] Build your toolbox strong and true Python ecosystem made for you Jupyter notebooks, experiment and explore Git and DVC, version control and more Set it up reproducible, that's the way to go Python AI tooling, everything you need to know [Bridge] Scikit-learn for classic ML, TensorFlow for the deep PyTorch research friendly, Hugging Face models to keep LangChain for the language tasks, frameworks all around End-to-end pipeline building, insights to be found [Verse 3] Load your public dataset, explore what's hiding there Clean and visualize the patterns, handle with such care Git commits save your progress, DVC tracks your data Reproducible research pipeline, you're a data gladiator [Chorus] Build your toolbox strong and true Python ecosystem made for you Jupyter notebooks, experiment and explore Git and DVC, version control and more Set it up reproducible, that's the way to go Python AI tooling, everything you need to know [Outro] From setup to deployment, tools that never fail Python AI ecosystem, you're ready for the trail
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