Python & C AI Upskilling Mentorship
Budget: ₹400 – ₹750 INR
I’m already comfortable with computer-science theory, yet my practical side needs a real push. My goal is to sharpen my Programming abilities—specifically in Python and C—by building AI/ML-oriented projects that go beyond textbook exercises.
Here’s the kind of support I’m after:
• A clear learning road-map that links core Python and C concepts to hands-on artificial-intelligence use-cases.
• Short, progressive coding challenges culminating in a mini end-to-end AI/ML project (model design, training, and deployment).
• Regular live or recorded code walk-throughs, detailed feedback, and refactoring advice so I learn idiomatic patterns in both languages.
• Explanations of underlying algorithms and data structures whenever they appear, tying low-level C logic to high-level Python libraries like NumPy, pandas, scikit-learn, or TensorFlow.
Acceptance criteria
1. A written study plan with milestones and estimated timelines.
2. Source-controlled sample projects demonstrating each milestone.
3. A final runnable AI/ML application in both Python and, where feasible, a performance-critical C component.
4. Concise documentation so I can revisit concepts independently.
If you’ve mentored learners before and can fluently bridge Python’s rapid prototyping with C’s systems-level efficiency, I’m eager to start.
Here’s the kind of support I’m after:
• A clear learning road-map that links core Python and C concepts to hands-on artificial-intelligence use-cases.
• Short, progressive coding challenges culminating in a mini end-to-end AI/ML project (model design, training, and deployment).
• Regular live or recorded code walk-throughs, detailed feedback, and refactoring advice so I learn idiomatic patterns in both languages.
• Explanations of underlying algorithms and data structures whenever they appear, tying low-level C logic to high-level Python libraries like NumPy, pandas, scikit-learn, or TensorFlow.
Acceptance criteria
1. A written study plan with milestones and estimated timelines.
2. Source-controlled sample projects demonstrating each milestone.
3. A final runnable AI/ML application in both Python and, where feasible, a performance-critical C component.
4. Concise documentation so I can revisit concepts independently.
If you’ve mentored learners before and can fluently bridge Python’s rapid prototyping with C’s systems-level efficiency, I’m eager to start.