Teen AI Course Facilitator
Budget: $25 – $50 AUD
Our new program introduces teenagers to Machine Learning through a blend of online and in-person experiences across Australia. The curriculum framework is drafted; now I need a facilitator who can:
• Record concise Python-based lessons (screen-share with voice-over, showing Jupyter Notebooks, scikit-learn, and simple TensorFlow examples).
• Host regular live Q&A sessions on Zoom so students can extend the pre-recorded material, troubleshoot code, and discuss real-world ML applications.
• Run hands-on workshops at selected campuses (interactive, project-driven, laptops open, small-group support). Safety, engagement, and clear learning outcomes are critical.
Key points to keep in mind
– Audience: 13-17 year olds with mixed coding backgrounds.
– Style: practical, friendly, visuals over equations, lots of mini-projects.
– Schedule: casual hours at first; frequency will scale with enrolments.
– Resources: I’ll provide a draft syllabus, LMS access, and marketing support—you refine content, deliver sessions, and suggest improvements.
Deliverables will be accepted when:
1. Ten polished video lessons (≈15 min each) are uploaded and follow the agreed outline.
2. At least two live Q&A sessions successfully run per online cohort (recordings supplied).
3. Each in-person workshop concludes with students demoing a working ML mini-project and submitting feedback forms showing ≥80 % positive engagement.
If you are energised by teaching teens, comfortable with both cameras and classrooms, and confident explaining ML concepts in plain English, let’s talk timelines and milestones.
• Record concise Python-based lessons (screen-share with voice-over, showing Jupyter Notebooks, scikit-learn, and simple TensorFlow examples).
• Host regular live Q&A sessions on Zoom so students can extend the pre-recorded material, troubleshoot code, and discuss real-world ML applications.
• Run hands-on workshops at selected campuses (interactive, project-driven, laptops open, small-group support). Safety, engagement, and clear learning outcomes are critical.
Key points to keep in mind
– Audience: 13-17 year olds with mixed coding backgrounds.
– Style: practical, friendly, visuals over equations, lots of mini-projects.
– Schedule: casual hours at first; frequency will scale with enrolments.
– Resources: I’ll provide a draft syllabus, LMS access, and marketing support—you refine content, deliver sessions, and suggest improvements.
Deliverables will be accepted when:
1. Ten polished video lessons (≈15 min each) are uploaded and follow the agreed outline.
2. At least two live Q&A sessions successfully run per online cohort (recordings supplied).
3. Each in-person workshop concludes with students demoing a working ML mini-project and submitting feedback forms showing ≥80 % positive engagement.
If you are energised by teaching teens, comfortable with both cameras and classrooms, and confident explaining ML concepts in plain English, let’s talk timelines and milestones.