Hands-On AI Learning Mentor
Budget: ₹10,000 – ₹13,000 INR
I’m a complete beginner who wants to move well beyond theory and into practical, project-based learning. The journey should start with data-science fundamentals—Python, NumPy, pandas, basic statistics, data cleaning and visualization—then progress step-by-step to machine-learning models, large-language-model workflows, retrieval-augmented generation (RAG), agent frameworks, and automation best practices.
Hands-on projects are essential; each topic needs to come with a small, self-contained exercise or micro-app that I can build, run, and extend. Think Jupyter notebooks, lightweight Flask or FastAPI endpoints, and scripts that hit real APIs (OpenAI, Hugging Face, LangChain, etc.). Code must be well-commented so I can retrace the logic afterward.
Ideal flow
• Kick-off call to map out a personalised syllabus
• Weekly live sessions (screen-sharing preferred) for explanations and coding together
• Between sessions: curated reading links, datasets, and clearly written tasks
• Code reviews with actionable feedback before we move on to the next module
Acceptance criteria
• Every lesson delivers a runnable project that demonstrates the concept in practice
• I can explain back what the code does and why we chose that technique
• Repository remains organised by topic with a README that ties the pieces together
If you enjoy teaching through building and can adapt pace as I progress, let’s start crafting the first dataset exploration notebook right away.
Hands-on projects are essential; each topic needs to come with a small, self-contained exercise or micro-app that I can build, run, and extend. Think Jupyter notebooks, lightweight Flask or FastAPI endpoints, and scripts that hit real APIs (OpenAI, Hugging Face, LangChain, etc.). Code must be well-commented so I can retrace the logic afterward.
Ideal flow
• Kick-off call to map out a personalised syllabus
• Weekly live sessions (screen-sharing preferred) for explanations and coding together
• Between sessions: curated reading links, datasets, and clearly written tasks
• Code reviews with actionable feedback before we move on to the next module
Acceptance criteria
• Every lesson delivers a runnable project that demonstrates the concept in practice
• I can explain back what the code does and why we chose that technique
• Repository remains organised by topic with a README that ties the pieces together
If you enjoy teaching through building and can adapt pace as I progress, let’s start crafting the first dataset exploration notebook right away.