Hands-On AI Learning Mentor

@chiragardeshna

Hi there, A strong fit for this work, with experience mentoring beginners through hands-on AI learning using real projects and clear, structured guidance. Clear understanding of your requirement to progress from Python fundamentals to ML, LLMs, RAG, and agent systems through practical, build-first sessions. Hands-on expertise ensures well-commented code, guided exercises, live coding sessions, and structured repositories for easy learning and revision. Risk stays minimized through step-by-step progression, regular feedback, and ensuring concepts are clearly understood before moving forward. Available to start immediately happy to plan your syllabus and begin with the first project. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@ashinnd84

Hello there, we are a team of Full Stack Developers. Please, send me the project complete details to start the work. Thanks Ashish Kumar.

@Ritesh11gupta

Hi! I noticed your project 'Hands-On AI Learning Mentor' and believe I'm the right person for it. I have relevant experience and can start immediately. My focus is always on understanding your exact needs first, then executing efficiently. Feel free to review my profile and let's discuss!

@Sadiyasyeda24

Hi, I have read your project details and I get what you need. I am an expert with 4 years of experience in Software Development, Automation. See my profile for recent work. Please connect in chat so we can discuss. Best regards, Syeda Tahreem

@abishek129

Hello, Your project aligns well with my experience in Python development, backend systems, and applied machine learning. I have over 2 years of experience building scalable applications using Python, Django, PostgreSQL, Redis, and AWS, and I’ve also worked with ML frameworks like TensorFlow and PyTorch. I’ve built data-driven systems such as recommendation engines and real-time processing pipelines, so guiding project-based learning in data science and AI would be a natural fit. I can help structure a practical learning path starting from Python, NumPy, pandas, statistics, and data visualization, then gradually moving toward machine learning models, LLM workflows, RAG systems, and AI agents. Each module will include a runnable project such as dataset exploration notebooks, ML models, API-based AI tools, and small FastAPI/Flask applications. All code will be well-commented and organized in a GitHub repository with clear README files so you can revisit concepts easily. I’m comfortable conducting weekly live coding sessions, reviewing assignments, and adjusting the pace based on your progress. We can start immediately with the first dataset exploration notebook and build the learning path step by step. Best regards Abishek

@aurvixlabs

You need a hands-on curriculum that moves from data science fundamentals to production-ready LLM workflows, with each lesson delivering a runnable, well-commented project. In the PolyGNN project, I built a complete ML system from data modeling to deployment, a process ideal for teaching through incremental builds. My AWS ML Specialty cert and deep experience with Python, FastAPI, and the full stack you listed (OpenAI, LangChain, RAG) map directly to your journey. I'd structure this into two clear milestones, ensuring you have working code from the first week. To tailor the initial dataset, what's a domain you're personally interested in for our first exploration notebook?

@satyaevolfast

Hello, I am excited about the opportunity to support your project with my expertise in AI, Python, and web development. My experience with Agentic AI, Computer Vision, and data-driven applications aligns well with your needs. For your project, I propose leveraging a modular architecture utilizing Flask for the backend, combined with React for a responsive frontend. We can utilize robust libraries like TensorFlow for machine learning and integrate APIs for enhanced functionality. The workflow would include initial data processing, model training, and continuous improvement based on user feedback. Recently, I developed Pixeal, an AI automated annotation platform, which involved similar data workflows and integration challenges. This experience can directly inform the successful execution of your project. If the project scope expands, I can also provide team support from ASPL. I’m looking forward to discussing final details further, ensuring we address all your requirements. Best regards, Satya

@nushratp

Hi, I can help you move from beginner to building real AI/ML projects step-by-step with a fully hands-on approach. ✔ Python, NumPy, pandas, EDA ✔ Machine Learning with real datasets ✔ LLMs, RAG, APIs (OpenAI, Hugging Face) ✔ FastAPI/Flask deployment How we’ll work: Weekly live coding sessions (screen share) Practical assignments + datasets Code reviews with clear feedback Organized GitHub repo with README Every session = a runnable project, so you don’t just learn—you build and understand. I can start immediately. Let’s begin with your first EDA notebook. Best regards,

@Mikun007

Hi, I can help you move from theory to real, practical skills through a structured, project-driven approach exactly as you described. My focus is on learning by building, with every concept tied to a working mini-project. We’ll start with Python, NumPy, pandas, and data visualization, then gradually progress to machine learning, LLM workflows. Each module will include a self-contained project (Jupyter notebooks, APIs using Flask/FastAPI, and real integrations like OpenAI/Hugging Face), with clear, well-commented code so you can revisit and understand everything. We can start immediately with your first data exploration notebook, where you’ll learn data cleaning, visualization, and extracting insights from real datasets. Let me know a good time for a quick kickoff discussion, and we’ll get started. Thanks!

@utsavk51

Hi, Your requirement for a hands-on, project-based AI learning journey aligns perfectly with how I approach teaching — by building real applications step by step. I specialize in guiding beginners from fundamentals to practical AI systems through structured, easy-to-follow projects. Here’s how I propose we work: • Start with a personalized roadmap based on your current level • Weekly live coding sessions (screen sharing) focused on building real projects • Each topic will include a runnable notebook or mini-app (Flask/FastAPI) • Clean, well-commented code so you can revise and understand independently • Regular code reviews with clear feedback before moving ahead Planned learning path: * Python, NumPy, pandas, data analysis * Visualization and real dataset projects * Machine learning fundamentals with hands-on models * LLM integrations (OpenAI, Hugging Face) * RAG systems and basic AI agents My goal is to ensure you don’t just run code, but actually understand and explain it confidently. We can begin with a dataset exploration notebook in the very first session. Looking forward to working together. Best regards, Utsav

@humayundev

Being an instructor for almost 14 years and working in the field of applied machine learning fit me for this project.

@alexandero60

Although I’m early in freelancing, I’ve already built practical projects that follow the exact path you’re looking for. For example: - A data analysis project using Python, pandas, and visualization - A simple machine learning model for prediction tasks - A basic LLM-powered workflow for answering questions from documents These projects helped me understand not just the theory, but how to apply each concept step-by-step—exactly what you’re aiming to achieve. Because I’ve recently gone through this learning path myself, I can guide you in a clear, beginner-friendly way without overwhelming you.

@minhd62

Hi! Teaching AI through building is exactly how I learn and work — this sounds like a great fit. Here's how I'd structure our sessions: Week 1-2 — Foundations: Python data science stack (pandas, NumPy, matplotlib) with a real dataset exploration notebook you build from scratch. Week 3-4 — Machine Learning: Scikit-learn models (regression, classification, clustering), plus a small end-to-end ML project with a FastAPI endpoint to serve predictions. Week 5-6 — LLMs & LangChain: OpenAI API basics, prompt engineering, then building a LangChain agent that can answer questions using tools (web search, calculator, etc.). Week 7-8 — RAG System: Connect LangChain to a vector database (FAISS or Chroma), load your own documents, build a working Q&A chatbot over them. Each module includes: a Jupyter notebook with well-commented code, a mini-project you can run and extend, and a README tying everything together. I adapt pace based on your comfort level — faster if something clicks quickly, slower if you want to go deeper. Weekly screen-sharing sessions work well for me. Shall we start with a kick-off call to map your personalised syllabus? I can send you the first dataset exploration notebook right away.