I want to build a solid, practical understanding of Machine Learning and then apply it confidently in real-world projects. Machine Learning is my immediate priority, yet I also need guidance in the supporting stack I use every day—Python for model building, SQL for data extraction, Excel for fast exploration, and Power BI for communicating results. Here is what I have in mind: • A structured series of live, screen-shared sessions (video or interactive notebook) that start with core ML theory and quickly move into code-along examples. • Short, targeted assignments after each meeting so I can test concepts on my own data sets. • Clear feedback on the work, including corrections and performance tips. • A growing library of notes or annotated notebooks that I can revisit later. Please feel free to suggest the best sequence of topics, preferred tools (Jupyter, VS Code, Power Query, etc.), and a cadence that balances depth with steady progress. My schedule is flexible, and I’m ready to begin as soon as we agree on a plan.